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awesome-agi-cocosci

An awesome & curated list for Artificial General Intelligence, an emerging inter-discipline field that combines artificial intelligence and computational cognitive sciences.
https://github.com/SHI-Yu-Zhe/awesome-agi-cocosci

Last synced: 3 days ago
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Academic Tools
- Paper Writing
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - bioRender - ***bioRender***. Create professional science figures in minutes by browsing thousands of pre-made icons and templates from more than 30 fields of life sciences.
  - How to construct a Nature summary paragraph - ***Nature***. Nature official guidelines for composing abstracts.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - Scientific Papers - ***Nature***. Nature guidance on writing scientific papers.
  - The Machine Learning Reproducibility Checklist - ***McGill University***. Guidelines for introducing a machine learning algorithm with guarantee of reproducibility.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - Scientific Papers - ***Nature***. Nature guidance on writing scientific papers.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
  - How to write a superb literature review - ***Nature***, 2020. Nature speaks to old hands and first timers about the work they did to make their reviews sing.
- Paper Reading
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - How to Read a Paper - ***ACM SIGCOMM Computer Communication Review***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=7234542241721187587&hl=en&as_sdt=0,5)]. A comprehensive tutorial on reading scientific papers.
  - How to (seriously) read a scientific paper - ***Science***, 2016. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=How+to+%28seriously%29+read+a+scientific+paper&btnG=)]. Science interview on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - How to navigate a scientific paper with time constraints: a graphics approach - ***MIT***. MIT guidance on strategies for reading papers given different time constraints.
  - Text Visualization Browser - ***ISOVIS group***, 2015. [[Paper](https://cs.lnu.se/isovis/pubs/docs/kucher-pacificvis15-postprint.pdf)]. [[All Versions](https://scholar.google.com/scholar?cluster=7000995325728444282&hl=en&as_sdt=0,5)]. A Hub of Text Visualization Techniques.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
  - It's not just you: science papers are getting harder to read - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4409814498614719804&hl=en&as_sdt=0,5)]. Nature perspective on reading scientific papers.
- Literature Management
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - How to keep up with the scientific literature - ***Science***, 2016. Science interview on organizing scientific papers.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Microsoft Academic Graph - ***Microsoft Research***. Heterogeneous graph containing scientific publication records, citation relationships between those publications, as well as authors, institutions, journals, conferences, and fields of study.
  - An Overview of Microsoft Academic Service (MAS) and Applications - ***WWW'15***, 2015. [[All Versios](https://scholar.google.com/scholar?cluster=9075899176667058496&hl=en&as_sdt=0,5)]. Original paper on Microsoft Academic Graph.
  - Semantic Scholar - ***Allen Institute for AI Research***. AI-powered scientific literature research tool.
  - Construction of the Literature Graph in Semantic Scholar - ***NAACL'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5500969515339734950&hl=en&as_sdt=0,5)]. Semantic Scholar with extracting feature and metadata from raw paper data.
  - S2ORC: The Semantic Scholar Open Research Corpus - ***ACL'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=11978464475399626925&hl=en&as_sdt=0,5)]. An open corpus of academic papers released by Semantic Scholar.
  - Litmaps - ***Litmap Ltd***. For interactive literature map construction and linked document management.
  - VOSviewer - ***Leiden University***. For constructing and visualizing bibliometric networks.
  - StateOfTheArt.AI - ***StateOfTheArtAI***. For tracking, collecting and visualizing the development of AI research.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Goodbye, Microsoft Academic – Hello, open research infrastructure? - ***LSE Impact Blog***, 2021. An interpretation of Microsoft's strategy on research infrastructure.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
  - Scientific literature: Information overload - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9898832432826237365&hl=en&as_sdt=0,5)]. Perspective on handling overloaded information from scientific literature.
- Courses
  - Computational Cognitive Science Courses - ***MIT***. Courses on computational cognitive science from MIT, Harvard, and Stanford.
  - Introduction to Program Synthesis - ***MIT***. Armando Solar-Lezama's elementary course on program synthesis.
  - Structure and Interpretation of Computer Programs - ***MIT***. [[Book: SICP](https://web.mit.edu/6.001/6.037/sicp.pdf)]. [[All Versions](https://scholar.google.com/scholar?cluster=7488066943428166450&hl=en&as_sdt=0,5)]. Classic course on applying structural, procedural, and meta-linguistic abstraction to solve computational problems.
  - Discrete Mathematics and Its Applications
- Programming
  - Probabilistic Models of Cognition - ***MIT***. The probabilistic approach to cognitive science, which models learning and reasoning as inference in complex probabilistic models.
- Knowledge Management
  - Library of Congress Classification - ***Library of Congress***. Classification system of USA (PDF only).
  - Chinese Library Classification - ***National Library of China***. Classification system of P. R. China (online user interface in Chinese). [[English introduction at ISKO](https://www.isko.org/cyclo/clc)]. [[Wikipedia-EN](https://en.wikipedia.org/wiki/Chinese_Library_Classification)].
  - DDC at German National Library - ***Deutsche National Bibliothek***. Deway Decimal Classification (DDC) based classification system of Germany (online user interface). [[DNB Website](https://www.dnb.de/EN/Professionell/DDC-Deutsch/DDCinDNB/ddcindnb_node.html)].
  - National Dite Library Classification - ***National Diet Library of Japan***. Classification system of Japan (PDF only).
  - DDC at OCLC (Wikipedia) - ***Online Computer Library Center (OCLC)***. [[OCLC Website](https://www.oclc.org/en/home.html)]. [[Introduction to DDC](https://www.oclc.org/content/dam/oclc/dewey/versions/print/intro.pdf)]. [[DDC Manual](https://www.oclc.org/content/dam/oclc/webdewey/help/full_manual.pdf)]. Dewey Decimal Classification (DDC) system for worldwide library resouce construction. [[DDC Class 000 (PDF only)](https://www.oclc.org/content/dam/oclc/webdewey/help/000.pdf)]. [[DDC Class 100 (PDF only)](https://www.oclc.org/content/dam/oclc/webdewey/help/100.pdf)]. [[DDC Class 200 (PDF only)](https://www.oclc.org/content/dam/oclc/webdewey/help/200.pdf)]. [[DDC Class 300 (PDF only)](https://www.oclc.org/content/dam/oclc/webdewey/help/300.pdf)]. [[DDC Class 400 (PDF only)](https://www.oclc.org/content/dam/oclc/webdewey/help/400.pdf)]. [[DDC Class 500 (PDF only)](https://www.oclc.org/content/dam/oclc/webdewey/help/500.pdf)]. [[DDC Class 600 (PDF only)](https://www.oclc.org/content/dam/oclc/webdewey/help/600.pdf)]. [[DDC Class 700 (PDF only)](https://www.oclc.org/content/dam/oclc/webdewey/help/700.pdf)]. [[DDC Class 800 (PDF only)](https://www.oclc.org/content/dam/oclc/webdewey/help/800.pdf)]. [[DDC Class 900 (PDF only)](https://www.oclc.org/content/dam/oclc/webdewey/help/900.pdf)].
  - Knowledge organization - ***Wikipedia***. Wikipedia on knowledge organization methods.
  - The Zettelkasten Method - ***Bielefeld University***. Relating ideas in graphs and multi-labels.
  - Zettelkasten - ***Wikipedia***. Wikipedia on the Zettelkasten method.
  - Roam Research - ***Roam Research***. For linked document management, visualization, and sharing.
  - Building a Second Brain - ***Forte Labs, LLC***. Connecting ideas in graphs.
  - Zotero - ***Digital Scholar***. For reference management to manage bibliographic data and research related materials.
  - Niklas Luhmann's Card Index: Thinking Tool, Communication Partner, Publication Machine - ***Forgetting Machines: Knowledge Management Evolution in Early Modern Europe, Brill***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=1786807670077004336&hl=en&as_sdt=0,5)].
  - The card index as creativity machine - ***Culture Machine***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=9767873312286889264&hl=en&as_sdt=0,5)].
  - Where Does Niklas Luhmann's Card Index Come From? - ***Erudition and the Republic of Letters***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=8279465066043884141&hl=en&as_sdt=0,5)]. A simplified introduction on Luhmann's Zettelkasten.
  - Niklas Luhmann's Card Index: The Fabrication of Serendipity - ***Sociologica***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12440286698665929622&hl=en&as_sdt=0,5)].
  - Communicating with Slip Boxes - 2019. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Communicating+with+slip+boxes+luhmann&btnG=)].
  - Chinese Library Classification - ***National Library of China***. Classification system of P. R. China (online user interface in Chinese). [[English introduction at ISKO](https://www.isko.org/cyclo/clc)]. [[Wikipedia-EN](https://en.wikipedia.org/wiki/Chinese_Library_Classification)].
Papers
- Abduction
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abduction - ***Plato Stanford***. A computational philosophy account on Abduction, one of the three thinking patterns besides Induction and Deduction, being unique for its potential to introduce new ideas into current knowledge.
  - Scientific Explanation - ***Plato Stanford***. A computational philosophy account on Scientific Explanation, a canonical application of Abduction.
  - Scientific Reduction - ***Plato Stanford***. A computational philosophy account on Scientific Reduction, which comes with no explicit boundary with Explanation.
  - Non-monotonic Logic - ***Plato Stanford***. A computational philosophy account on Non-monotonic Logic, a family of formal frameworks devised to capture and represent defeasible inference.
  - The Inference to the Best Explanation - ***Philosophical Review***, 1965. [[All Versions](https://scholar.google.com/scholar?cluster=1416627814151433560&hl=en&as_sdt=0,5)]. Lipton's original paper on Inference to the Best Explanation as a special case of Abduction.
  - Inference to the Best Explanation - ***Routledge***, 1991. [[All Versions](https://scholar.google.com/scholar?cluster=6494546505729177895&hl=en&as_sdt=0,5)]. Lipton's book on Inference to the Best Explanation as a special case of Abduction.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - A Study of Thinking - ***Routledge***, 1956. [[All Versions](https://scholar.google.com/scholar?cluster=17466297915128086930&hl=en&as_sdt=0,5)]. A classic book on thinking patterns.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Abductive Reasoning: Logical Investigations into Discovery and Explanation - ***Springer***, 2006. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Abductive+Reasoning%3A+Logical+Investigations+into+Discovery+and+Explanation&btnG=)]. An introductory account on abductive reasoning.
  - Explanation and Abductive Inference - ***The Oxford Handbook of Thinking and Reasoning***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=16126850654692681562&hl=en&as_sdt=0,5)]. A handbook on the formulations of Abduction.
  - Probabilistic models of cognition: Conceptual foundations - ***Trends in Cognitive Sciences***, 2006. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=12857321660837478492)]. A Bayesian account of Abduction.
  - The structure and function of explanations - ***Trends in Cognitive Sciences***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=2849189270394400667&hl=en&as_sdt=0,5)]. Basic computation modes of Abduction.
  - Explanatory Preferences Shape Learning and Inference - ***Trends in Cognitive Sciences***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=2040551538203889465&hl=en&as_sdt=0,5)]. An account showing that inductive bias is critical for explanation.
  - The Role of Explanatory Considerations in Updating - ***Cognition***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=3089358487428261042&hl=en&as_sdt=0,5)].
  - How explanation guides belief change - ***Trends in Cognitive Sciences***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=15240531165875981526&hl=en&as_sdt=2005&sciodt=2005)]. A review on the subjective probability account of Abduction.
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - Use of current explanations in multicausal abductive reasoning - ***Cognitive Science***, 2001. [[All Versions](https://scholar.google.com/scholar?cluster=7816050625957759346&hl=en&as_sdt=2005&sciodt=0,5)].
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - Abduction: A categorical characterization - ***Journal of Applied Logic***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=17834260152484836885&hl=en&as_sdt=2005&sciodt=0,5)].
  - Defending Abduction - ***Philosophy of Science***, 1999. [[All Versions](https://scholar.google.com/scholar?cluster=13895790050138832555&hl=en&as_sdt=0,5)].
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Towards an Architecture for Cognitive Vision Using Qualitative Spatio-temporal Representations and Abduction - ***Spatial Cognition***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=8072265283930278310&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - Probabilistic alternatives to Bayesianism: the case of explanationism - ***Frontiers in Psychology***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=9016714668469830914&hl=en&as_sdt=0,5)]. A non-Bayesian account of Abduction.
  - A Probabilistic Theory of Abductive Reasoning - ***ICAART***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=450937566244876051&hl=en&as_sdt=0,5)]. A probabilistic perspective for interpreting Abductive Reasoning.
  - The order effect in human abductive reasoning: an empirical and computational study - ***Journal of Experimental & Theoretical Artificial Intelligence***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3803536062463585043&hl=en&as_sdt=0,5)].
  - Abduction, Induction, and Analogy - ***Model-Based Reasoning in Science and Technology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=14979764682921693390&hl=en&as_sdt=0,5)]. The distinctions and relations between Abduction, Induction, and Analogy.
  - Remembrance of inferences past: Amortization in human hypothesis generation - ***Cognition***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=190340622765037472&hl=en&as_sdt=2005&sciodt=0,5)]. A rational account of human hypothesis generation.
  - The AHA! Experience: Creativity Through Emergent Binding in Neural Networks - ***Cognitive Science***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=10006889101167052798&hl=en&as_sdt=0,5)].
  - Explanation-seeking curiosity in childhood - ***Current Opinion in Behavioral Sciences***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4167956555501133663&hl=en&as_sdt=2005)]. A piece of developmental pshchological evidence for Abduction in young children.
  - Scientific Discovery - ***Plato Stanford***. A computational philosophy account on Scientific Discovery, the process or product of successful scientific inquiry, sometimes an Abduction-like (Explanation) thinking pattern.
  - Models of Discovery: And Other Topics in the Methods of Science - ***Springer***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=9932701864897299105&hl=en&as_sdt=0,5)]. The original book on search as scientific thinking.
  - Induction: Processes of Inference, Learning, and Discovery - ***MIT Press***, 1989. [[All Versions](https://scholar.google.com/scholar?cluster=12402938838725132707&hl=en&as_sdt=0,5)]. An Induction account of Scientific Discovery.
  - Dual Space Search During Scientific Reasoning - ***Cognitive Science***, 1988. [[All Versions](https://scholar.google.com/scholar?cluster=17542852673494089523&hl=en&as_sdt=2005&sciodt=0,5)]. The original paper on the dual space search as scientific thinking theory.
  - Complexity Management in a Discovery Task - ***CogSci'92***, 1992. [[All Versions](https://scholar.google.com/scholar?cluster=18138712608977258974&hl=en&as_sdt=2005&sciodt=0,5)]. Advanced experiments on dual space search.
  - A dual-space model of iteratively deepening exploratory learning - ***International Journal of Human-Computer Studies***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=17337189265334825678&hl=en&as_sdt=2005&sciodt=0,5)]. Iterative version (in depth and in width) of dual space search.
  - Coalescing the Vapors of Human Experience into a Viable and Meaningful Comprehension - ***CogSci'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=5460385008324352958&hl=en&as_sdt=2005&sciodt=0,5)]. Constrainted thinking as rationalization.
  - How We Know What Not To Think - ***Trends in Cognitive Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13106919756521743226&hl=en&as_sdt=2005&sciodt=0,5)]. A comprehensive review on rationalization.
  - Rationalization is rational - ***Behavioral and Brain Sciences***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=5165464589274056844&hl=en&as_sdt=2005&sciodt=0,5)]. A rationality account on rationalization.
  - Rationalizing constraints on the capacity for cognitive control - ***Trends in Cognitive Sciences***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=13065113821339619145&hl=en&as_sdt=2005&sciodt=0,5)].
  - Heuristics for Scientific Experimentation: A Developmental Study - ***Cognitive Psychology***, 1993. [[All Versions](https://scholar.google.com/scholar?cluster=2469515962071844494&hl=en&as_sdt=2005&sciodt=0,5)]. A piece of evidence on children have basic scientific thinking skills.
  - A 4-Space Model of Scientific Discovery - ***CogSci'95***, 1995. [[All Versions](https://scholar.google.com/scholar?cluster=1063157789682040473&hl=en&as_sdt=2005&sciodt=0,5)]. Extending the dual space search.
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Confirmation, disconfirmation, and information in hypothesis testing - ***Psychological Review***, 1987. [[All Versions](https://scholar.google.com/scholar?cluster=1954141597807453515&hl=en&as_sdt=0,5)]. A psychological account on hypothesis testing.
  - Hypothesis generation, sparse categories, and the positive test strategy - ***Psychological Review***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=4329636480235863472&hl=en&as_sdt=2005&sciodt=0,5)].
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  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
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  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
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  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159)]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.
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  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Why Imaginary Worlds? The psychological foundations and cultural evolution of fictions with imaginary worlds - ***Behavioral and Brain Sciences***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=16985691366494688837)]. A review of rationalization as imaginary worlds in fictions. The perspective proposes that imaginary worlds co-opt our preferences for exploration, which have evolved in humans and nonhuman animals alike, to propel individuals toward new environments and new sources of reward.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Philosophical Writings of Peirce - ***Courier Corporation***, 1955. [[All Versions](https://scholar.google.com/scholar?cluster=3917019015464129592)]. Original writings by C. S. Peirce, the philosopher who first introduces the concept of Abduction.
  - Inference to the Best Explanation - ***Routledge***, 1991. [[All Versions](https://scholar.google.com/scholar?cluster=5097986614430666854)]. Lipton's original paper on Inference to the Best Explanation as a specialized condition of Abduction.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159)]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.
  - Abductive Cognition: The Epistemological and Eco-Cognitive Dimensions of Hypothetical Reasoning - ***Springer***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=8707351442527595188)]. Most philosophers of science in the twentieth century have concluded that no logic of creative processes exists and, moreover, that a rational model of discovery is impossible. In short, scientific creative inferences are irrational and there is no “reasoning” to hypotheses. On the other hand, some research in the area of artificial intelligence has shown that methods for discovery could be found that are computationally adequate for rediscovering – or discovering for the first time – empirical or theoretical laws and theorems.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Human Comprehensible Active Learning of Genome-Scale Metabolic Networks - ***AAAI Spring Symposium Series 2023 on Computational Scientific Discovery***, 2023. [[All Versions](https://scholar.google.com/scholar?oi=bibs&cluster=10875437066608527790)]. [[Extended Abstract](http://cogsys.org/symposium/discovery-2023/abstracts/Abstract_3169.pdf)]. [[Slides](http://cogsys.org/symposium/discovery-2023/talks/Ai.pdf)]. This work introduces a novel machine learning framework ILP-iML1515 based on Inductive Logic Programming (ILP) that performs abductive logical reasoning and actively learns from training examples. The ILP-iML1515 framework 1) allows high-throughput simulations and 2) actively selects experiments that reduce the experimental cost of learning gene functions in comparison to randomly selected experiments.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159)]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159)]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - The order effect in human abductive reasoning: an empirical and computational study - ***Journal of Experimental & Theoretical Artificial Intelligence***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3803536062463585043&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159)]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.
  - Probabilistic models of cognition: Conceptual foundations - X) - ***Trends in Cognitive Sciences***, 2006. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=12857321660837478492)]. Remarkable progress in the mathematics and computer science of probability has led to a revolution in the scope of probabilistic models. In particular, ‘sophisticated’ probabilistic methods apply to structured relational systems such as graphs and grammars, of immediate relevance to the cognitive sciences. This review outlines progress in this rapidly developing field, which provides a potentially unifying perspective across a wide range of domains and levels of explanation.
  - Best, second-best, and good-enough explanations: How they matter to reasoning - ***Journal of Experimental Psychology***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=3067550385175104201)]. There is a wealth of evidence that people’s reasoning is influenced by explanatory considerations. Three experiments investigate the descriptive adequacy of a precise proposal to be found in the philosophical literature, to wit, that we should infer to the best explanation, provided certain additional conditions are met. The main conslusions are that (a) the quality of an explanation is a good predictor of people’s willingness to accept that explanation, and a better predictor than the prior probability of the explanation, and (b) if more than one possible explanation is given, people are the less willing to infer the best explanation the better they deem the second-best explanation.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159)]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159)]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159)]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Kinematic mental simulations in abduction and deduction - ***Proceedings of the National Academy of Sciences***, 2013. [[All Versions](https://scholar.google.com/scholar?cluster=11864820390497230588)]. This paper presents a theory, and its computer implementation, of how mental simulations underlie the abductions of informal algorithms and deductions from these algorithms. Three experiments tested the theory’s predictions, using an environment of a single railway track and a siding. The results corroborated the use of a kinematic mental model in creating and testing informal algorithms and showed that individuals differ reliably in the ability to carry out these tasks.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Automated Biodesign Engineering by Abductive Meta-Interpretive Learning - ***AAAI Spring Symposium Series 2021 on Artificial Intelligence for Synthetic Biology***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=543730388062329581&as_sdt=0,5)]. This work proposes an automated biodesign engineering framework empowered by Abductive Meta-Interpretive Learning (MetaAbd), a novel machine learning approach that combines symbolic and sub-symbolic machine learning, to further enhance the design-build-test-learn cycle by enabling the learning machine to 1) exploit domain knowledge and learn human-interpretable models that are expressed by formal languages such as first-order logic; 2) simultaneously optimise the structure and parameters of the models to make accurate numerical predictions; 3) reduce the cost of experiments and effort on data annotation by actively generating hypotheses and examples.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159)]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159)]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Explanatory Preferences Shape Learning and Inference - ***Trends in Cognitive Sciences***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=2040551538203889465&hl=en&as_sdt=0,5)]. An account showing that inductive bias is critical for explanation.
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159)]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Scientific discovery: Computational explorations of the creative processes - ***MIT Press***, 1987. [[All Versions](https://scholar.google.com/scholar?cluster=11327000316248254911)]. The book is divided into four parts. Part I introduces the subject of discovery, defines the scope of our work, and discusses some of the issues that have surrounded and still surround our topic. Parts II and III contain the main body of our results, largely in the form of accounts of the performance of computer programs that simulate human thought processes to make scientific discoveries. Part II is devoted largely to the processes for inducing quantitative theories from data. Part III is devoted mainly to the processes for inducing qualitative descriptive and structural theories from data. In Part IV, on the basis of our experience, we discuss at a lower level of precision how the programs described in the preceding chapters could be combined into a single, more general discovery system, and we describe a wide range of the other component processes that enter into scientific discovery.
  - Exploring science: The cognition and development of discovery processes - ***MIT Press***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=13091264356550286420)]. In this book, D. Klahr sets out to describe the cognitive and developmental processes that have enabled scientists to make the discoveries that comprise the body of information we call "scientific knowledge." Over the past decade, Klahr and his colleagues have conducted laboratory experiments in which they create discovery contexts, computer-based environments, to evoke the kind of thinking characteristic of scientific discovery in the "real world." In attempting to solve the problems posed by the discovery tasks, experiment participants (from preschoolers to university students) use many of the same higher-order cognitive processes used by practicing scientists. Through his work, Klahr integrates two disparate approaches–the content-based approach and the process-based approach– to present a comprehensive model of the psychology of scientific discovery.
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159)]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - The Role of Explanatory Considerations in Updating - ***Cognition***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=3089358487428261042)]. This paper investigates experimentally controversy in philosophy about the connection between explanation and inference, of whether judgments of the explanatory goodness of hypotheses do play a role when people revise their degrees of belief in those hypotheses upon the receipt of new evidence.
  - How explanation guides belief change - ***Trends in Cognitive Sciences***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=15240531165875981526)]. Philosophers have argued that people ought to change their graded beliefs via Bayes’ rule. Recent work in psychology indicates that people sometimes violate that rule by attending to explanatory factors. Results from computational modeling suggest that such violations may actually be rational.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - A Probabilistic Theory of Abductive Reasoning - ***ICAART***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=450937566244876051&hl=en&as_sdt=0,5)]. A probabilistic perspective for interpreting Abductive Reasoning.
  - A dual-space model of iteratively deepening exploratory learning - ***International Journal of Human-Computer Studies***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=17337189265334825678)]. This paper describes a cognitive model of exploratory learning, which covers both trial-and-error and instruction-taking activities. The model, implemented in Soar, is grounded in empirical data of subjects in a task-oriented, trial-and-error exploratory learning situation. A key empirical finding reflected in the model is the repeated scanning of a subset of the available menu items, with increased attention to items on each successive scan. This is explained in terms of dual search spaces, the external interface and the user's internal knowledge, both of which must be tentatively explored with attention to changing costs and benefits.
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159)]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159)]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Explanation, updating, and accuracy - ***Journal of Cognitive Psychology***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=967127146748155733)]. There is evidence that people update their credences partly on the basis of explanatory considerations. Philosophers have recently argued that to minimise the inaccuracy of their credences, people's updates also ought to be partly based on such considerations. However, there are many ways in which explanatory considerations can factor into updating, not all of which minimise inaccuracy. It is an open question whether in their updating, people take explanatory considerations into account in a way that philosophers would deem recommendable. To address this question, the authors re-analyse data from an experiment reported in Douven and Schupbach, “The role of explanatory considerations in updating”.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159&hl=en&as_sdt=0,5)]. An introductory account on abductive reasoning.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  - Functional genomic hypothesis generation and experimentation by a robot scientist - ***Nature***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=17461972625475533182&hl=en&as_sdt=0,5)]. A canonical application of logical abduction on biodesign.
  - Highly accurate protein structure prediction with AlphaFold - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6286436358625670901&hl=en&as_sdt=0,5)]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  - Abductive Inference in Bayesian Networks: A Review - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
  - Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159)]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.
  - Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  - On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  - Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  - Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  - Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  - When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
- Concepts
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - Concepts - ***Plato Stanford***. A collection of the computational philosophical debates about the concepts.
  - Theory-theory - ***Wikipedia***. Wikipedia for the Theory theory, a perspective that contextualizes concepts in theoretical (or empirical) systems.
  - Words, thoughts, and theories - ***MIT Press***, 1997. [[All Versions](https://scholar.google.com/scholar?cluster=16726462136203686735&hl=en&as_sdt=0,5)]. Alison Gopnik's book that articulates and defends the "theory theory" of cognitive and semantic development, the idea that infants and young children, like scientists, learn about the world by forming and revising theories-a view of the origins of knowledge and meaning that has broad implications for cognitive science.
  - The Theory Theory - ***Mapping the mind: Domain specificity in cognition and culture, Cambridge University Press***, 1994. [[All Versions](https://scholar.google.com/scholar?cluster=9397889700764191662&hl=en&as_sdt=0,5)]. Alison Gopnik's original paper on the theory theory.
  - What we mean when we say semantic: A Consensus statement on the nomenclature of semantic memory - 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7464626532716945232&hl=en&as_sdt=0,5)]. The aim of this multidisciplinary workgroup was to establish consensus definitions for some of the major recurring constructs in semantic research (e.g., concept, amodal, abstract). These efforts yielded a glossary consisting of succinct definitions, agreement, subjective confidence ratings, relevant theoretical background, and principled dissenting views. These core definitions will potentially yield benchmarks for aligning perspectives and improving cross-disciplinary communication in semantic research.
  - Similarity involving attributes and relations: Judgments of similarity and difference are not inverses - ***Psychological Science***, 1990. [[All Versions](https://scholar.google.com/scholar?cluster=13205938250772079784&hl=en&as_sdt=0,5)]. Theory on similarity judgement by attributes and relations.
  - Organizing conceptual knowledge in humans with a gridlike code - ***Science***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=10995575332310321503&hl=en&as_sdt=0,5)]. Original findings suggest that global relational codes may be used to organize nonspatial conceptual representations and that these codes may have a hexagonal gridlike pattern when conceptual knowledge is laid out in two continuous dimensions.
  - Navigating cognition: Spatial codes for human thinking - ***Science***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=1407237757770081862&hl=en&as_sdt=0,5)]. A framework that operates across information domains to support a wide spectrum of cognitive functions, where place and grid cell population codes provide a representational format to map variable dimensions of cognitive spaces.
  - Structuring Knowledge with Cognitive Maps and Cognitive Graphs - ***Trends in Cognitive Sciences***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=7196012353183004425&hl=en&as_sdt=0,5)]. Russel Epstein's review on evidence suggesting that both map-like and graph-like representations exist in the mind/brain that rely on partially overlapping neural systems.
  - Idiosyncratic Tower of Babel: Individual differences in word-meaning representation increase as word abstractness increases - ***Psychological Science***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=18214600097352809308&hl=en&as_sdt=0,5)]. Uncovering the cognitive and neural origins of word-meaning disagreements across individuals.
  - Using a high-dimensional graph of semantic space to model relationships among words - ***Frontiers in Psychology***, 2014. [[All Versions](https://scholar.google.com/scholar?cluster=472523411548302295&hl=en&as_sdt=0,5)]. First-order similarity and second-order relation metrics for word embedding.
  - Simple shape feature computation across modalities: convergence and divergence between the ventral and dorsal visual streams - ***Cerebral Cortex***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=5977822802446917081&hl=en&as_sdt=0,5)]. Visual and haptic shape perception fMRI experiments suggesting that mid-level shape features are represented in a modality-independent manner in both the ventral and dorsal streams.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - A principal odor map unifies diverse tasks in olfactory perception - ***Science***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17847258457660438418&hl=en&as_sdt=0,5)]. [[Code](https://github.com/osmoai/publications/tree/main/lee_et_al_2023)]. [[Data (Reproduced)](https://www.kaggle.com/datasets/aryanamitbarsainyan/multi-labelled-smiles-odors-dataset)]. [[Preprint](https://centaur.reading.ac.uk/113304/1/Mayhew%20et%20al%20for%20Centaur.pdf)]. [[GoodScents Database](https://www.thegoodscentscompany.com/)]. [[Leffingwell Database](http://www.leffingwell.com/bacispmp.htm)]. A Principal Odor Map (POM) that preserves perceptual relationships and enables odor quality prediction for novel odorants.
  - Metabolic activity organizes olfactory representations - ***eLife***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8857896396450033667&hl=en&as_sdt=0,5)]. [[Code & Data](https://github.com/osmoai/publications/tree/main/qian_et_al_2023)]. Odorous compounds with similar POM representations are more likely to co-occur within a substance and be metabolically closely related; metabolic reaction sequences also follow smooth paths in POM despite large jumps in molecular structure.
  - A Review of Tactile Information: Perception and Action Through Touch - ***IEEE Transactions on Robotics***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=15493221881484741343&hl=en&as_sdt=0,5)]. [[ResearchGate](https://www.researchgate.net/profile/Qiang-Li-110/publication/342797645_A_Review_of_Tactile_Information_Perception_and_Action_Through_Touch/links/602f95bc92851c4ed5806e9f/A-Review-of-Tactile-Information-Perception-and-Action-Through-Touch.pdf)]. A hierarchy consisting of raw, contact, object, and action levels to structure the tactile information.
  - ImageBind: One Embedding Space To Bind Them All - ***CVPR'23***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=1657173986906232916&hl=en&as_sdt=0,5)]. [[Project](https://github.com/facebookresearch/ImageBind)]. Cross-modality representation fusion by aligning all other modalities to the visual modality.
  - Semantic features of object concepts generated with GPT-3 - ***CogSci'22***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16958563995984242923&hl=en&as_sdt=0,5)]. Testing the semantic attributes of the concepts generated by the large language model GPT-3.
  - Connecting Touch and Vision via Cross-Modal Prediction - ***CVPR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=17326564895972374001&hl=en&as_sdt=0,5)]. [[Project](https://github.com/YunzhuLi/VisGel)].
  - Unit Testing for Concepts in Neural Networks - ***Transactions of the Association for Computational Linguistics***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=3036662275506971282&hl=en&as_sdt=0,5)]. Testing the concept representation by neural networks through Fodor's theory of concepts.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - Locating what comes to mind in empirically derived representational spaces - ***Cognition***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=57834483230365927&hl=en&as_sdt=0,5)]. An evidence-based study concluding that people call category members to mind according to their location in representational space, specifically based on the predicted usefulness of considering category members with particular features.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - Natural speech reveals the semantic maps that tile human cerebral cortex - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14997953800741854188&hl=en&as_sdt=0,5)]. [[Code & Tutorial](https://github.com/HuthLab/speechmodeltutorial)]. Systematically mapping semantic selectivity across the cortex using voxel-wise modelling of functional MRI data collected while subjects listened to hours of narrative stories, showing that the semantic system is
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - Do Llamas Work in English? On the Latent Language of Multilingual Transformers - 2024. [[All Versions](https://scholar.google.com/scholar?cluster=5847238732288003106&hl=en&as_sdt=0,5)]. A preliminary work empirically showing that the intermediate embeddings of multilingual Transformers (1) start far away from output token embeddings; (2) already allow for decoding a semantically correct next token in the middle layers, but give higher probability to its version in English than in the input language; (3) finally move into an input-language-specific region of the embedding space. Also, the embedding of abstract concept space lies closer to English than to other languages.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - Semantic projection recovers rich human knowledge of multiple object features from word embeddings - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=2499199921371106654&hl=en&as_sdt=0,5)]. Proposing a domain-general method to extract context-dependent relationships from word embeddings: ‘semantic projection’ of word-vectors onto lines that represent multiple dimensions of features, which recovers human judgements across various object categories and properties.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
  - The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies - ***Scientific Data***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4039754406289857135&hl=en&as_sdt=0,5)]. [[Project](https://clics.clld.org/)]. CLICS tackles interconnected interdisciplinary research questions about the colexifcation of words across semantic categories in the world’s languages, and show-cases best practices for preparing data for cross-linguistic research.
- Communications
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - The Interactive Evolution of Human Communication Systems - ***Cognitive Science***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=6689941517686043970)]. Nicolas Fay's original paper on iconicity.
  - Iconicity: From sign to system in human communication and language - ***Pragmatics & Cognition***, 2014. [[All Versions](https://scholar.google.com/scholar?cluster=8525760321117094567&hl=en&as_sdt=0,5)]. Nicolas Fay's account on the emergence of iconic language.
  - The Picture Exchange Communication System - ***Behavior Modification***, 1994. [[All Versions](https://scholar.google.com/scholar?cluster=18113491434570143349&hl=en&as_sdt=0,5)].
  - Graphical Language Games: Interactional Constraints on Representational Form - ***Cognitive Science***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=280214578402050136&hl=en&as_sdt=0,5)]. The first paper introducing the graphical language game.
  - A multimodal discourse theory of visual narrative - ***Journal of Pragmatics***, 2014. [[All Versions](https://scholar.google.com/scholar?cluster=912273653379961242&hl=en&as_sdt=0,5)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Emergent Graphical Conventions in a Visual Communication Game - 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6476453985812346727&hl=en&as_sdt=0,5)]. A computational account on the emergence of iconic language.
  - Communicating artificial neural networks develop efficient color-naming systems - ***Proceedings of the National Academy of Sciences***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1640459156303560508&hl=en&as_sdt=0,5)]. Simulating the emergence of code as the communication bottleneck in color learning task.
  - Bridging cultural and cognitive perspectives on similarity reasoning - ***CogSci'22***, 2022. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Bridging+cultural+and+cognitive+perspectives+on+similarity+reasoning&btnG=)].
  - Twelve-month-olds communicate helpfully and appropriately for knowledgeable and ignorant partners - ***Cognition***, 2008. [[All Versions](https://scholar.google.com/scholar?cluster=8202048572661677635&hl=en&as_sdt=0,5)]. The original paper on child pointing.
  - 12- and 18-Month-Olds Point to Provide Information for Others - ***Journal of Cognition and Development***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=7322772656439413984&hl=en&as_sdt=0,5)].
  - Pragmatics - ***Plato Stanford***. A computational philosophy account of Pragmatics, whilch studies utterances in specific contexts.
  - Predicting Pragmatic Reasoning in Language Games - ***Science***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=15533081031935746054)]. The original paper on Rational Speech Act (RSA).
  - Pragmatic Language Interpretation as Probabilistic Inference - ***Trends in Cognitive Sciences***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11393505968563356130&hl=en&as_sdt=0,5)]. Noah Goodman and Micheal Frank's review on Rational Speech Act.
  - Processing gradable adjectives in context: A visual world study - ***Semantics and Linguistic Theory***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=13426776838629402579)]. Adjective understanding as a rational inference in the context.
  - Pragmatic Issue-Sensitive Image Captioning - ***ACL Findings: EMNLP'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=10608257248144445301&hl=en&as_sdt=0,5)]. Application of Rational Speech Act to Image Captioning.
  - Disentangling contributions of visual information and interaction history in the formation of graphical conventions - ***CogSci'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15046353579508199394&hl=en&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - Information Structure in Discourse: Towards an Integrated Formal Theory of Pragmatics - ***Semantics and Pragmatics***, 1998. [[All Versions](https://scholar.google.com/scholar?cluster=9127222314768938599&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - The SocialAI School: Insights from Developmental Psychology Towards Artificial Socio-Cultural Agents - ***ICML'23 Workshop on Theory-of-Mind***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11933410239580707313&hl=en&as_sdt=0,5)]. [[Project](https://sites.google.com/view/socialai-school)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Exploring Urban Form Through Openstreetmap Data: A Visual Introduction - ***Urban Experience and Design: Contemporary Perspectives on Improving the Public Realm***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7094530618542001733&hl=en&as_sdt=0,5)]. [[OSMnx Tool](https://github.com/gboeing/osmnx)]. [[OpenStreetMap Website]( https://www.openstreetmap.org/)].
  - Saying what you mean in dialogue: A study in conceptual and semantic co-ordination - ***Cognition***, 1987. [[All Versions](https://scholar.google.com/scholar?cluster=15377075954534820544&hl=en&as_sdt=0,5)].
  - Conversation, co-ordination and convention: an empirical investigation of how groups establish linguistic conventions - ***Cognition***, 1994. [[All Versions](https://scholar.google.com/scholar?cluster=3784850469297049700&hl=en&as_sdt=0,5)].
  - Compositionality - ***Plato Stanford***. A computational philosophy account on compositionality, one of the distinctive feature of language.
  - The Principle of Semantic Compositionality - ***Topoi***, 1994. [[All Versions](https://scholar.google.com/scholar?cluster=10899040818001759322&hl=en&as_sdt=0,5)]. The original paper on the principle of semantic compositionality.
  - On The Emergence Of Compositionality - ***Proceedings of the Evolution of Language Conference'06***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=16315741180717951222&hl=en&as_sdt=0,5)]. The original paper on the emergence of compositionality.
  - Multi-Agent Cooperation and the Emergence of (Natural) Language - ***ICLR'17***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=1931070702879918446&hl=en&as_sdt=0,5)]. The original paper on the emergence of language in multi-agent reinforcement learning.
  - Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols - ***NeurIPS'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17308624474306270808&hl=en&as_sdt=0,5)].
  - Emergent communication through negotiation - ***ICLR'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=8825869866742501521&hl=en&as_sdt=0,5)].
  - The language of generalization - ***Psychological Review***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7723877614160376324&hl=en&as_sdt=0,5)].
  - Compositionality and Generalization in Emergent Languages - ***ACL'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=5792073344743965767&hl=en&as_sdt=0,5)].
  - Word formation supports efficient communication: The case of compounds - ***CogSci'22***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=17465553221758916299&hl=en&as_sdt=0,5)].
  - War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars - 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3598519753107761968&hl=en&as_sdt=0,5)].
  - From Explicit Communication to Tacit Cooperation: A Novel Paradigm for Cooperative MARL - 2023. [[All Versions](https://scholar.google.com/scholar?cluster=12114270828108588849&hl=en&as_sdt=0,5)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - Pragmatic Reasoning through Semantic Inference - ***Semantics & Pragmatics***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=1433855075217315997)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding - ***Transactions of the Association for Computational Linguistics***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=11119271811833503059)].
  - Social Pragmatics: Preschoolers Rely on Commonsense Psychology to Resolve Referential Underspecification - ***Child Development***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=16352913537004112920)]. A piece of evidence for children's capability on social pragmatics.
  - Pragmatically Informative Image Captioning with Character-Level Inference - ***NAACL'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=1670953084401884599)].
  - Pixelor: A Competitive Sketching AI Agent. So you think you can beat me? - ***ACM SIGGRAPH'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6676723059377806081&hl=en&as_sdt=0,5)]. [[Project](http://sketchx.ai/pixelor)]. Rationality in feature sketching.
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - How young children integrate information sources to infer the meaning of words - ***Nature Human Behavior***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844)]. Before formal education begins, children typically acquire a vocabulary of thousands of words. This learning process requires the use of many different information sources in their social environment, including their current state of knowledge and the context in which they hear words used. This paper specifies a developmental model according to which children consider information sources in an age-specific way and integrate them via Bayesian inference. This work presents a developmental theory of information integration during language learning and illustrates how formal models can be used to make a quantitative test of the predictive and explanatory power of competing theories.
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - In situ bidirectional human-robot value alignment - ***Science Robotics***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=18342339995965564405)]. [[Preprint](https://par.nsf.gov/servlets/purl/10351399)]. This paper proposes an explainable artificial intelligence (XAI) system in which a group of robots predicts users’ values by taking in situ feedback into consideration while communicating their decision processes to users through explanations. To learn from human feedback, the XAI system integrates a cooperative communication model for inferring human values associated with multiple desirable goals. To be interpretable to humans, it simulates human mental dynamics and predicts optimal explanations using graphical models.
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature Human Behavior***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844)]. Before formal education begins, children typically acquire a vocabulary of thousands of words. This learning process requires the use of many different information sources in their social environment, including their current state of knowledge and the context in which they hear words used. This paper specifies a developmental model according to which children consider information sources in an age-specific way and integrate them via Bayesian inference. This work presents a developmental theory of information integration during language learning and illustrates how formal models can be used to make a quantitative test of the predictive and explanatory power of competing theories.
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature Human Behavior***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844)]. Before formal education begins, children typically acquire a vocabulary of thousands of words. This learning process requires the use of many different information sources in their social environment, including their current state of knowledge and the context in which they hear words used. This paper specifies a developmental model according to which children consider information sources in an age-specific way and integrate them via Bayesian inference. This work presents a developmental theory of information integration during language learning and illustrates how formal models can be used to make a quantitative test of the predictive and explanatory power of competing theories.
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature Human Behavior***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844)]. Before formal education begins, children typically acquire a vocabulary of thousands of words. This learning process requires the use of many different information sources in their social environment, including their current state of knowledge and the context in which they hear words used. This paper specifies a developmental model according to which children consider information sources in an age-specific way and integrate them via Bayesian inference. This work presents a developmental theory of information integration during language learning and illustrates how formal models can be used to make a quantitative test of the predictive and explanatory power of competing theories.
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature Human Behavior***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844)]. Before formal education begins, children typically acquire a vocabulary of thousands of words. This learning process requires the use of many different information sources in their social environment, including their current state of knowledge and the context in which they hear words used. This paper specifies a developmental model according to which children consider information sources in an age-specific way and integrate them via Bayesian inference. This work presents a developmental theory of information integration during language learning and illustrates how formal models can be used to make a quantitative test of the predictive and explanatory power of competing theories.
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844&hl=en&as_sdt=0,5)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - How young children integrate information sources to infer the meaning of words - ***Nature Human Behavior***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844)]. Before formal education begins, children typically acquire a vocabulary of thousands of words. This learning process requires the use of many different information sources in their social environment, including their current state of knowledge and the context in which they hear words used. This paper specifies a developmental model according to which children consider information sources in an age-specific way and integrate them via Bayesian inference. This work presents a developmental theory of information integration during language learning and illustrates how formal models can be used to make a quantitative test of the predictive and explanatory power of competing theories.
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature Human Behavior***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844)]. Before formal education begins, children typically acquire a vocabulary of thousands of words. This learning process requires the use of many different information sources in their social environment, including their current state of knowledge and the context in which they hear words used. This paper specifies a developmental model according to which children consider information sources in an age-specific way and integrate them via Bayesian inference. This work presents a developmental theory of information integration during language learning and illustrates how formal models can be used to make a quantitative test of the predictive and explanatory power of competing theories.
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature Human Behavior***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844)]. Before formal education begins, children typically acquire a vocabulary of thousands of words. This learning process requires the use of many different information sources in their social environment, including their current state of knowledge and the context in which they hear words used. This paper specifies a developmental model according to which children consider information sources in an age-specific way and integrate them via Bayesian inference. This work presents a developmental theory of information integration during language learning and illustrates how formal models can be used to make a quantitative test of the predictive and explanatory power of competing theories.
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
  - Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17971107104483505071&hl=en&as_sdt=0,5)]. A computational account on the rational behavior in graphical language games.
  - Toward understanding the importance of gesture in distributed scientific collaboration - ***Knowledge and Information Systems***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3145646721897130511&as_sdt=0,5)].
  - How young children integrate information sources to infer the meaning of words - ***Nature Human Behavior***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10144794357802769844)]. Before formal education begins, children typically acquire a vocabulary of thousands of words. This learning process requires the use of many different information sources in their social environment, including their current state of knowledge and the context in which they hear words used. This paper specifies a developmental model according to which children consider information sources in an age-specific way and integrate them via Bayesian inference. This work presents a developmental theory of information integration during language learning and illustrates how formal models can be used to make a quantitative test of the predictive and explanatory power of competing theories.
  - When Lingens meets Frege: communication without common ground - ***Philosophical Studies***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10912415595149303257&hl=en&as_sdt=0,5)].
  - Language as shaped by the environment: linguistic construal in a collaborative spatial task - ***Nature Humanities and Social Sciences Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7842508027049437987&hl=en&as_sdt=0,5)]. [[Code & Data](https://osf.io/sxtaq)]. [[Dialogue Experimental Toolkit(DiET)](https://dialoguetoolkit.github.io/chattool/)].
- Problem Solving
  - People construct simplified mental representations to plan - ***Nature***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=12068944400080889789&hl=en&as_sdt=0,5)]. A computational account on rational problem representation in human planning.
  - Learning to perceive and act by trial and error - ***Machine Learning***, 1991. [[All Versions](https://scholar.google.com/scholar?cluster=1987606770603964473&hl=en&as_sdt=0,5)].
  - Neural Task Programming: Learning to Generalize Across Hierarchical Tasks - ***ICRA'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=7155333517647976638&hl=en&as_sdt=0,5)].
  - Elements of a theory of human problem solving - ***Psychological Review***, 1958. [[All Versions](https://scholar.google.com/scholar?cluster=6226995019045187501&hl=en&as_sdt=0,5)]. Herbert Simon's original idea on human problem solving.
  - Human Problem Solving - ***Englewood Cliffs, NJ: Prentice-hall***, 1972. [[All Versions](https://scholar.google.com/scholar?cluster=3996229083126262536&hl=en&as_sdt=0,5)]. Herbert Simon's classic idea of human problem solving as search.
  - Learning to Solve Problems: A Handbook for Designing Problem-Solving Learning Environments - ***Taylorfrancis***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=13262690779319271809&hl=en&as_sdt=2005&sciodt=0,5)].
  - Judgment under Uncertainty: Heuristics and Biases: Biases in judgments reveal some heuristics of thinking under uncertainty - ***Science***, 1974. [[All Versions](https://scholar.google.com/scholar?cluster=17040257859216791312&hl=en&as_sdt=0,5)]. Daniel Kahneman's classic idea of prospective theory.
  - Computational evidence for hierarchically structured reinforcement learning in humans - ***Proceedings of the National Academy of Sciences***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=5731363475904675608&hl=en&as_sdt=0,5)]. A piece of evidence on hierarchical human planning.
  - Hierarchical reasoning by neural circuits in the frontal cortex - ***Science***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=9875733886908769773&hl=en&as_sdt=0,5)]. Neuroscience evidence supporting rule switch.
  - The importance of mixed selectivity in complex cognitive tasks - ***Nature***, 2013. [[All Versions](https://scholar.google.com/scholar?cluster=2707751672275136220&hl=en&as_sdt=0,5)]. The original paper introducing mixed selectivity with high-dimensional neural representations.
  - People construct simplified mental representations to plan - ***Nature***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=12068944400080889789&hl=en&as_sdt=0,5)]. A computational account on rational problem representation in human planning.
  - Goals, usefulness and abstraction in value-based choice - ***Trends in Cognitive Sciences***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=6256990098976657651&hl=en&as_sdt=2005&sciodt=0,5)]. A review that outlines the computational and biological principles that enable the brain to compute the usefulness of an option or action by creating abstractions that flexibly adapt to changing goals.
  - Value signals guide abstraction during learning - ***eLife***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10324834842795908439&hl=en&as_sdt=0,5)].
  - Learning to perceive and act by trial and error - ***Machine Learning***, 1991. [[All Versions](https://scholar.google.com/scholar?cluster=1987606770603964473&hl=en&as_sdt=0,5)].
  - Representations in distributed cognitive tasks - ***Cognitive Science***, 1994. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=14781266698447195483)].
  - The nature of external representations in problem solving - ***Cognitive Science***, 1997. [[All Versions](https://scholar.google.com/scholar?cluster=10698887231200401430&hl=en&as_sdt=0,5)].
  - Rapid trail-and-error learning with simulation supports flexible tool use and physical reasoning. - ***Proceedings of the National Academy of Sciences***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=14400178089019636923&hl=en&as_sdt=0,5)]. [[Project](https://sites.google.com/view/virtualtoolsgame/home)]. [[Appendix](https://www.pnas.org/content/pnas/suppl/2020/11/20/1912341117.DCSupplemental/pnas.1912341117.sapp.pdf)]. A computational account on rapid trail-and-error problem solving with a noisy prior model.
  - Abstract strategy learning underlies flexible transfer in physical problem solving - ***CogSci'20***, 2020. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Abstract+strategy+learning+underlies+flexible+transfer+in+physical+problem+solving.&btnG=)].
  - Physion: Evaluating Physical Prediction from Vision in Humans and Machines - ***NeurIPS'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=8733318111076645893&hl=en&as_sdt=0,5)].
  - Exploration: from machines to humans - ***Current Opinion in Behavioral Sciences***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=8015078432419172621&hl=en&as_sdt=0,5)].
  - Balancing exploration and exploitation with information and randomization - ***Current Opinion in Behavioral Sciences***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=8164388137243077863&hl=en&as_sdt=2005&sciodt=0,5)].
  - Hippocampal neurons construct a map of an abstract value space - ***Cell***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12658820581876003172&hl=en&as_sdt=0,5)].
  - Insightful problem solving and creative tool modification by captive nontool-using rooks - ***Proceedings of the National Academy of Sciences***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=6776471679661065229&hl=en&as_sdt=0,5)]. [[Supplementary Material](https://www.pnas.org/content/suppl/2009/05/28/0901008106.DCSupplemental)]. A piece of evidence on creative tool use in intelligent animals.
  - Learning to act by integrating mental simulations and physical experiments - ***CogSci'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=7342920174595829739&hl=en&as_sdt=0,5)]. [[Code](https://github.com/ishita-dg/SimulationVSAction)].
  - The successor representation in human reinforcement learning - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7317529612823134939&hl=en&as_sdt=0,5)].
  - From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning - ***Journal of Artificial Intelligence Research***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17962480659445514879&hl=en&as_sdt=0,5)]. Leslie Kaelbling's review on hierarchical Task-and-Motion-Planning (hierarchical TAMP).
  - Integrated Task and Motion Planning - ***Annual Review of Control, Robotics, and Autonomous Systems***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=478421650694199529&hl=en&as_sdt=0,5)]. Leslie Kaelbling's review on Task-and-Motion-Planning (TAMP).
  - Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning - ***Robotics: Science and Systems***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10342169019935480143&hl=en&as_sdt=0,5)].
  - Learning to act by integrating mental simulations and physical experiments - ***CogSci'21***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=7342920174595829739&hl=en&as_sdt=0,5)].
  - What Is the Model in Model-Based Planning? - ***Cognitive Science***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10598397017491369972&hl=en&scisbd=1&as_sdt=2005&sciodt=0,5)].
  - Discovering State and Action Abstractions for Generalized Task and Motion Planning - ***AAAI'22***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=1054368060554971920)].
  - Intrinsically Motivated Reinforcement Learning - ***NeurIPS'04***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=9736217847061704054&hl=en&as_sdt=0,5)]. A comprehensive review on intrinsic reward functions in classic reinforcement learning.
  - What is intrinsic motivation? A typology of computational approaches - ***Frontiers in Neurorobotics***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=11901343819872275353&hl=en&as_sdt=0,5)].
  - Adapting Behavior via Intrinsic Reward: A Survey and Empirical Study - ***Journal of Artificial Intelligence Research***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=5309595875334344707&hl=en&as_sdt=0,5)].
  - Curiosity-driven Exploration by Self-supervised Prediction - ***ICML'17***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9379743003299559904&hl=en&as_sdt=0,5)]. The original paper on curiosity as intrinsic motivation.
  - UCB Exploration via Q-Ensembles - 2017. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=13260404166621290240)].
  - Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning - ***ICML'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=4880520597219138666&hl=en&as_sdt=0,5)].
  - Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning - ***NeurIPS'15***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=9262504233068870193&hl=en&as_sdt=0,5)]. The original paper on empowerment as intrinsic motivation.
  - Multi-task reinforcement learning in humans - ***Nature Human Behavior***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=14589018692074515644&hl=en&as_sdt=0,5)].
  - Reinforcement learning: An introduction - ***MIT Press***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=8821915215029978039&hl=en&as_sdt=0,5)]. Richard Sutton's comprehensive book on reinforcement learning.
  - Reinforcement learning: A survey - ***Journal of Artificial Intelligence Research***, 1996. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=4983604491168613713)]. Leslie Kaelbling's review on reinforcement learning.
  - An overview of multi-agent reinforcement learning from game theoretical perspective - 2020. [[All Versions](https://scholar.google.com/scholar?cluster=16197919002723407603&hl=en&as_sdt=0,5)]. Yaodong Yang's review on multi-agent reinforcement learning from the perspective of game theory.
  - Human-level control through deep reinforcement learning - ***Nature***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=12439121588427761338&hl=en&as_sdt=0,5)]. The original paper on solving Atari games via Deep Q-Network.
  - Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning - ***Artificial Intelligence***, 1999. [[All Versions](https://scholar.google.com/scholar?cluster=1471968208408231068&hl=en&as_sdt=0,5)]. The original paper on operation reinforcement learning.
  - On Monte Carlo Tree Search and Reinforcement Learning - ***Journal of Artificial Intelligence Research***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=5805718077259491860&hl=en&as_sdt=0,5)].
  - Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review - 2018. [[All Versions](https://scholar.google.com/scholar?cluster=16437288987337534404&hl=en&as_sdt=0,5)]. [[Slides](http://rail.eecs.berkeley.edu/deeprlcourse-fa18/static/slides/lec-15.pdf)]. Sergey Levine's tutorial on treating reinforcement learning probabilisticly.
  - A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation - ***NeurIPS'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7721047641895252765&hl=en&as_sdt=0,5)].
  - Solving Compositional Reinforcement Learning Problems via Task Reduction - ***ICLR'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=15628616147808752058&hl=en&as_sdt=0,5)].
  - Neural Task Programming: Learning to Generalize Across Hierarchical Tasks - ***ICRA'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=7155333517647976638&hl=en&as_sdt=0,5)].
  - Learning to act: qualitative learning of deterministic action models - ***Journal of Logic and Computation***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=14570482854600886953&hl=en&as_sdt=0,5)].
  - Learning to Act and Observe in Partially Observable Domains - 2021. [[All Versions](https://scholar.google.com/scholar?cluster=2258600434630687063&hl=en&as_sdt=0,5)].
  - Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability - ***NeurIPS'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=9640851185758072663&hl=en&as_sdt=0,5)]. A formal treatment on the generalization problem in reinforcement learning.
  - Learning to Perform Physics Experiments via Deep Reinforcement Learning - ***ICLR'17***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=13142558595749186250&hl=en&as_sdt=0,5)].
  - Data-Efficient Learning for Complex and Real-Time Physical Problem Solving Using Augmented Simulation - ***Robotics and Automation Letters***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=3140653562829320759&hl=en&as_sdt=0,5)].
  - A Survey of Preference-Based Reinforcement Learning Methods - ***Journal of Machine Learning Research***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=13278778479251450967&hl=en&as_sdt=0,5)].
  - On the Expressivity of Markov Reward - ***NeurIPS'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=4524686816939437211&hl=en&as_sdt=0,5)]. A formal treatment of tasks and rewards in reinforcement learning modeling.
  - Trust Region Policy Optimization - ***ICML'15***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=4215501129336400677&hl=en&as_sdt=0,5)]. The original paper introducing TRPO, a method for optimizing control policies, with guaranteed monotonic improvement.
  - Constrained Policy Optimization - ***ICML'17***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6114366704163518185&hl=en&as_sdt=0,5)]. The original paper on constrained reinforcement learning (safe reinforcement learning).
  - When to Trust Your Model: Model-Based Policy Optimization - ***NeurIPS'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4248859125840907707&hl=en&as_sdt=0,5)]. [[Post](https://bair.berkeley.edu/blog/2019/12/12/mbpo/)].
  - SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning - ***ICML'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=8840831494454574191&hl=en&as_sdt=0,5)]. [[Code](https://github.com/pokaxpoka/sunrise)].
  - The Quest for a Common Model of the Intelligent Decision Maker - ***Multi-disciplinary Conference on Reinforcement Learning and Decision Making'22***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=7652784232757502910&hl=en&as_sdt=0,5)]. Richard Sutton's perspective on the future directions of reinforcement learning research.
  - Automatic curriculum learning for deep RL: a short survey - ***IJCAI'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=10660055557098312214&hl=en&as_sdt=0,5)].
  - TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL - ***ICML'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=11016662361926634008&hl=en&as_sdt=0,5)]. [[Project](https://github.com/flowersteam/TeachMyAgent)].
  - Apprenticeship Learning via Inverse Reinforcement Learning - ***ICML'04***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=10260011060619377707&hl=en&as_sdt=0,5)]. Pieter Abbeel and Andrew Ng's original paper on inverse reinforcement learning (IRL).
  - Bayesian Inverse Reinforcement Learning - ***IJCAI'07***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=4154724070362583557&hl=en&as_sdt=0,5)]. A Bayesian account on classic inverse reinforcement learning.
  - From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following - ***ICLR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=9128320307925997063&hl=en&as_sdt=0,5)].
  - Few-shot Bayesian imitation learning with logical program policies. - ***AAAI'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=5103854692762145813&hl=en&as_sdt=0,5)].
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  - Dual coding of knowledge in the human brain - ***Trends in Cognitive Sciences***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=11751507203561842501&hl=en&as_sdt=0,5)]. Yanchao Bi's review on neuroscience experiments on dual coding theory.
  - Two Forms of Knowledge Representations in the Human Brain - ***Neuron***, 2020. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=16941965185680116049)]. Illustrating language-derived and sensory-derived knowledge.
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  - A continuous semantic space describes the representation of thousands of object and action categories across the human brain - ***Neuron***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=10348115268396987731&hl=en&as_sdt=0,5)].
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - DeepProbLog: Neural Probabilistic Logic Programming - ***NeurIPS'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=6079567413300944995&hl=en&as_sdt=0,5)]. The original paper on neuro-symbolic probabilistic programming.
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  - Neural Logic Reinforcement Learning - ***ICML'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=18074632043038701502&hl=en&as_sdt=0,5)].
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  - Abductive Knowledge Induction From Raw Data - ***IJCAI'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=7027142960863064076&hl=en&as_sdt=0,5)].
  - Fast Abductive Learning by Similarity-based Consistency Optimization - ***NeurIPS'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=8539963460239876225&hl=en&as_sdt=0,5)]. An approach for accelerating the convergence of Abductive Learning.
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  - Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning - ***ICML'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=9257372000778020812&hl=en&as_sdt=0,5)].
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  - Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components - ***NeurIPS'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=12691103404451941071&hl=en&as_sdt=0,5)].
  - Neuro-Symbolic Visual Reasoning: Disentangling “Visual” from “Reasoning” - ***ICML'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13160160974887139307&hl=en&as_sdt=0,5)].
  - Understanding Deep Architectures with Reasoning Layer - ***NeurIPS'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=937882599430270789&hl=en&as_sdt=0,5)].
  - An Explicitly Relational Neural Network Architecture - ***ICML'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=37732747764322837&hl=en&as_sdt=0,5)].
  - Neural Production Systems - ***ICML'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=15299280949648915581&hl=en&as_sdt=0,5)]. Yoshua Bengio's perspective on slot attention model as a general production system.
  - Compositional Generalization via Neural-Symbolic Stack Machines - ***NeurIPS'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=15612498612943317331&hl=en&as_sdt=0,5)].
  - Stochastic Optimization of Sorting Networks via Continuous Relaxations - ***ICLR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=10619362619006891050&hl=en&as_sdt=0,5)].
  - Program Guided Agent - ***ICLR'20***, 2020. [[All Versions](https://openreview.net/forum?id=BkxUvnEYDH)].
  - Learning Compositional Rules via Neural Program Synthesis - ***NeurIPS'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=3160670555314650508&hl=en&as_sdt=0,5)].
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  - Neural Logic Machines - ***ICLR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4525183211642569463&hl=en&as_sdt=0,5)].
  - The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision - ***ICLR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8837128214653317831&hl=en&as_sdt=0,5)].
  - Visual Concept-Metaconcept Learning - ***NeurIPS'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=1888051343232298875&hl=en&as_sdt=0,5)].
  - Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning - ***ICLR'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=16735976343684307244&hl=en&as_sdt=0,5)].
  - Temporal and Object Quantification Networks - ***IJCAI'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=17251222943638414124&hl=en&as_sdt=0,5)].
  - Grounded Language Learning Fast and Slow - ***ICLR'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=17735027444431750346&hl=en&as_sdt=0,5)]. [[Project](https://github.com/deepmind/dm_fast_mapping?s=05)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Visual Programming: Compositional Visual Reasoning Without Training - ***CVPR'23***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=16156060658942400125&hl=en&as_sdt=0,5)]. VISPROG, a neuro-symbolic approach to solving complex and compositional visual tasks given natural language instructions, using the in-context learning ability of large language models to generate python-like modular programs, which are then executed to get both the solution and a comprehensive and interpretable rationale.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Learn to explain efﬁciently via neural logic inductive learning - ***ICLR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4550874980727321525&hl=en&as_sdt=0,5)]. [[Project](https://github.com/gblackout/NLIL)].
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
  - Rational arbitration between statistics and rules in human sequence processing - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=9856085207409198966&hl=en&as_sdt=0,5)].
  - Regression Analysis for Interval-Valued Data - ***Data Analysis, Classification, and Related Methods***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=9407097855380377791&hl=en&as_sdt=0,5)]. The original paper on symbolic regression.
  - Symbolic data analysis: what is it? - ***Proceedings in Computational Statistics***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3730437602749399283&hl=en&as_sdt=0,5)].
  - Abductive learning: towards bridging machine learning and logical reasoning - ***Science China Information Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8541635351775190855&hl=en&as_sdt=0,5)].
  - Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10321228117236432485&hl=en&as_sdt=0,5)]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
- Explainability
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - A tale of two explanations: Enhancing human trust by explaining robot behavior - ***Science Robotics***, 2019. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=3985046411399524590)]. The original paper on human believable robot, a result of the DAPAR-XAI.
  - X-ToM: Explaining with Theory-of-Mind for Gaining Justified Human Trust - 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7751326666821697923&hl=en&as_sdt=0,5)]. Introducing the idea of AI estimating human users' knowledge in to explainable AI.
  - CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines - ***AAAI'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17443137068166403183&hl=en&as_sdt=0,5)].
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Beneficial and harmful explanatory machine learning - ***Machine Learning***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=16983722694047294963&hl=en&as_sdt=0,5)].
  - Deep Forest: Towards An Alternative to Deep Neural Networks - ***IJCAI'17***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7391596872731517007&hl=en&as_sdt=0,5)]. [[Project](https://github.com/LAMDA-NJU/Deep-Forest)].
  - NBDT: Neural-Backed Decision Trees - ***NeurIPS'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1902399007162005819&hl=en&as_sdt=0,5)]. [[Code](https://github.com/alvinwan/neural-backed-decision-trees)]. Expliciting the decision process of a decision tree through neural networks.
  - Network dissection: Quantifying interpretability of deep visual representations - ***CVPR'17***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=18069685615852396783&hl=en&as_sdt=0,5)]. [[Project](http://netdissect.csail.mit.edu/)]. [[Dataset: Places365](http://places2.csail.mit.edu/index.html)]. The original paper on visualizing the class activation maps to explain convolutional neural networks.
  - Understanding the role of Individual Units in a Deep Neural Network - ***Proceedings of the National Academy of Sciences***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=11996680970579301810&hl=en&as_sdt=0,5)]. David Bau's review on network dissection for discriminative and generative models.
  - Zoom In: An Introduction to Circuits - ***Distill***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=9053581372570691569&hl=en&as_sdt=0,5)]. A perspective on treating neural networks as circuits.
  - Compositional Explanations of Neurons - ***NeurIPS'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=15725346730266402738&hl=en&as_sdt=0,5)]. [[Project](https://github.com/jayelm/compexp)]. A concept-composition version of network dissection.
  - This Looks Like That: Deep Learning for Interpretable Image Recognition - ***NeurIPS'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=9461838581952136719&hl=en&as_sdt=0,5)].
  - Unsupervised learning by competing hidden units - ***Proceedings of the National Academy of Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=1228003598355915526&hl=en&as_sdt=0,5)].
  - Noise or Signal: The Role of Backgrounds in Image Classification - ***ICLR'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=14729938011425134088&hl=en&as_sdt=0,5)]. [[Code & Data](https://github.com/MadryLab/backgrounds_challenge)]. [[Project](https://gradientscience.org/background/)]. A perspective on image background provides strong clue for foreground classification.
  - Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation - ***NeurIPS'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=401428033641216502&hl=en&as_sdt=0,5)]. Maching the learned pattern of neurons in different neural networks.
  - Individual differences among deep neural network models - ***Nature Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=8259893575188417318&hl=en&as_sdt=2005&sciodt=0,5)].
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Bayesian modeling of human–AI complementarity - ***Proceedings of the National Academy of Sciences***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=15735143859968841009&hl=en&as_sdt=0,5)]. A Bayesian framework for combining the predictions and different types of confidence scores from humans and machines.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - CX-ToM: Counterfactual explanations with theory-of-mind for enhancing human trust in image recognition models - ***iScience***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=17526041764295337444)].
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  - Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=17551060457946144913&hl=en&as_sdt=0,5)]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
- Embodied Intelligence
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Embodied Cognition - ***Plato Stanford***. A computational philosophy account on Embodied Cognition, which emphasizes the significance of an agent's physical body in cognitive abilities.
  - Externalism About the Mind - ***Plato Stanford***. A computational philosophy account on mind externalism, a long-term debate about the boundary of embodied intelligence.
  - Cognitive engineering: Human problem solving with tools - ***Human Factors***, 1988. [[All Versions](https://scholar.google.com/scholar?cluster=14194840995416222723&hl=en&as_sdt=0,5)]. The original idea of investigating huamn tool use in problem solving.
  - Tools, language and cognition in human evolution - ***Cambridge University Press***, 1993. [[All Versions](https://scholar.google.com/scholar?cluster=6046350461147957220&hl=en&as_sdt=0,5)]. A classic perspective correlating human tool use with the evolution of civilization.
  - The Extended Mind - ***Analysis***, 1998. [[All Versions](https://scholar.google.com/scholar?cluster=9546561188261943866&hl=en&as_sdt=0,5)]. The original paper on the debate of mind externalism.
  - The neural bases of complex tool use in humans - ***Trends in Cognitive Sciences***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=3612212926196611828&hl=en&as_sdt=0,5)]. A neuroscience account of human tool use.
  - Spontaneous Metatool Use by New Caledonian Crows - ***Current Biology***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=9263531730425342443&hl=en&as_sdt=0,5)]. A piece of evidence that intelligent animals can take advantage of matatools to make tools for problem solving.
  - Rapid Assimilation of External Objects Into the Body Schema - ***Psychological Science***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=854636910326733489&hl=en&as_sdt=0,5)].
  - The cognitive bases of human tool use - ***Behavioral and Brain Sciences***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=4648150119820414671&hl=en&as_sdt=0,5)].
  - The embodied mind extended: using words as social tools - ***Frontiers in Psychology***, 2013. [[All Versions](https://scholar.google.com/scholar?cluster=14719988081062606352&hl=en&as_sdt=0,5)].
  - Tool use as adaptation - ***Philosophical Transactions of the Royal Society B: Biological Sciences***, 2013. [[All Versions](https://scholar.google.com/scholar?cluster=8060841461200774807&hl=en&as_sdt=0,5)].
  - Intensive tool-practice and skillfulness facilitate the extension of body representations in humans - ***Neuropsychologia***, 2014. [[All Versions](https://scholar.google.com/scholar?cluster=10578024091098127929&hl=en&as_sdt=0,5)].
  - Tool use and affordance: Manipulation-based versus reasoning-based approaches - ***Psychological Review***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=3284942486402374505&hl=en&as_sdt=0,5)]. A classic review on human tool use and affordance.
  - Meta-strategy learning in physical problem-solving: the effect of embodied experience - ***CogSci'21***, 2021. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=9713842177532954702)].
  - Understanding Tools: Task-Oriented Object Modeling, Learning and Recognition - ***CVPR'15***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=4609926671953500969&hl=en&as_sdt=0,5)]. [[Project](https://yzhu.io/publication/tool2015cvpr/)]. The original paper introducing affordance and physically-grounded tool use into computer vision.
  - Robotic hand augmentation drives changes in neural body representation - ***Science Robotics***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1622125726197763917&hl=en&as_sdt=0,5)].
  - Expert Tool Users Show Increased Differentiation between Visual Representations of Hands and Tools - ***Journal of Neuroscience***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=13454164767827515188&hl=en&as_sdt=0,5)].
  - Visual scoping operations for physical assembly - ***CogSci'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=7238090583833839&hl=en&as_sdt=0,5)].
  - Behavior-grounded representation of tool affordances - ***ICRA'05***, 2005. [[All Versions](https://scholar.google.com/scholar?cluster=6115815663915603675&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight - ***RSS'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4316276917607326251&hl=en&as_sdt=0,5)].
  - Meta-strategy learning in physical problem-solving: the effect of embodied experience - 2021. [[All Versions](https://scholar.google.com/scholar?cluster=9713842177532954702&hl=en&as_sdt=0,5)].
  - 3D dynamic scene graphs: Actionable spatial perception with places, objects, and humans - ***RSS'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4428742298455436054&hl=en&as_sdt=0,5)]. A system for modeling 3D dynamic scene graphs on multiple levels (metric-semantic mesh, objects and agents, places and structures, rooms, and buildings).
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
  - Relational affordances for multiple-object manipulation - ***Autonomous Robots***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6357646940615855682&hl=en&as_sdt=0,5)].
  - A Relational Approach to Tool-Use Learning in Robots - ***ILP'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=18374178227592386332&hl=en&as_sdt=0,5)].
- Evolutionary Intelligence
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Evolutionary trade-offs, Pareto optimality, and the geometry of phenotype space - ***Science***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=16162252507845975080&hl=en&as_sdt=0,5)]. A classic paper correlating biological trade-offs with the evolution of pareto optimality.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies - ***IEEE Transactions on Systems, Man, and Cybernetics***, 2008. [[All Versions](https://scholar.google.com/scholar?cluster=11308312498510305429&hl=en&as_sdt=0,5)]. A comprehensive review on the application of pareto optimality to multiobjective machine learning.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - From language development to language evolution: A unified view of human lexical creativity - ***Science***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=15871163761816546924&hl=en&as_sdt=0,5)]. [[Preprint](https://brochhagen.github.io/content/ms/accepted-lexical-creativity.pdf)]. This work supports a unified foundation for human lexical creativity underlying both the fleeting products of individual ontogeny and the evolutionary products of phylogeny across languages.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13913123623752818925&hl=en&as_sdt=0,5)]. A Bayesian phylogenetic analysis on two competing hypotheses of the origin of the Sino-Tibetan language family suggests that the initial expansion of Sino-Tibetan languages occurred approximately 4,000–6,000 years before present (BP; taken as AD 1950) in the Yellow River basin of northern China, and that this expansion is associated with the development of the Yangshao and/or Majiayao Neolithic cultures.
  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
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  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
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  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
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  - Triangulation supports agricultural spread of the Transeurasian languages - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1183005894965630508&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-021-03037-w)]. A triangulation of linguistic, archaeological and genetic data suggests that the Transeurasian language family originated in a population of grain farmers in China around 9,000 years ago, and that agriculture underpinned its spread.
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  - Pareto optimality in multiobjective problems - ***Applied Mathematics and Optimization***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=11305142600366783354&hl=en&as_sdt=0,5)]. The original paper on the pareto optimality in multiobjective problems.
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- Methodologies for Experiments
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  - Scaling up experimental social, behavioral, and economic science - ***Open Science Foundation Preprints***. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Scaling+up+experimental+social%2C+behavioral%2C+and+economic+science&btnG=)]. A white paper on scaling up social, behavioral, and econimic experiments.
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  - Scaling up psychology via Scientific Regret Minimization - ***Proceedings of the National Academy of Sciences***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=8011895688226766944&hl=en&as_sdt=0,5)]. The statistical and ecological basis for scaling up behavioral studies.
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  - Using large-scale experiments and machine learning to discover theories of human decision-making - ***Science***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=7456250222852859810&hl=en&as_sdt=0,5)]. A piece of evidence for the merits brought by large-scale behavioral studies in social science.
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  - Exploring human cognition using large image databases - ***Topics in Cognitive Sciences***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=3629906005701226294&hl=en&as_sdt=0,5)].
  - Visual Search at Pinterest - ***KDD'15***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=2051024301293529405&hl=en&as_sdt=0,5)]. Large scale user study in the development of the recommendations system by Pinterest.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
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  - Evidence integration in model-based tree search - ***Proceedings of the National Academy of Sciences***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=11085043350027609187&hl=en&as_sdt=0,5)].
  - People Infer Recursive Visual Concepts from Just a Few Examples - ***Computational Brain & Behavior***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=3871396883970734141&hl=en&as_sdt=0,5)].
  - One-shot learning of generative speech concepts - ***CogSci'14***, 2014. [[All Versions](https://scholar.google.com/scholar?cluster=15482292457660075957&hl=en&as_sdt=0,5)].
  - Human few-shot learning of compositional instructions - ***CogSci'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=12841163907815018136&hl=en&as_sdt=0,5)].
  - Fast and flexible: Human program induction in abstract reasoning tasks - ***CogSci'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=5294483826040237516&hl=en&as_sdt=0,5)].
  - Investigating Human Priors for Playing Video Games - ***ICML'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=2202192690517876762&hl=en&as_sdt=0,5)].
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  - Implicit Association Test - ***Wikipedia***. Wikipedia on the Implicit Association Test, a controversial assessment intended to detect subconscious associations between mental representations of objects (concepts) in memory.
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  - A Meta-Analysis on the Correlation Between the Implicit Association Test and Explicit Self-Report Measures - ***Personality and Social Psychology Bulletin***, 2005. [[All Versions](https://scholar.google.com/scholar?cluster=4888328728717829047&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - Virtual reality: A survival guide for the social scientist - ***Journal of Media Psychology***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=17318470193315023264&hl=en&as_sdt=0,5)].
  - How Immersive Is Enough? A Meta-Analysis of the Effect of Immersive Technology on User Presence - ***Media Psychology***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=9218122072360464558&hl=en&as_sdt=0,5)]. A meta-analysis on the extent to which technologies need to be immersive in order to generate a sense of presence.
  - Towards an Understanding of Distributed Asymmetric Collaborative Visualization on Problem-solving - ***VR'23***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11228377215337222005&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - Experiments with More Than One Random Factor: Designs, Analytic Models, and Statistical Power - ***Annual Review of Psychology***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6652444619934494760&hl=en&as_sdt=0,5)]. A comprehensive review of the quantitative analysis techniques for behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
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  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
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  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
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  - Health of the Implicit Association Test at age 3 - ***Zeitschrift für Experimentelle Psychologie***, 2001. [[All Versions](https://scholar.google.com/scholar?cluster=10868478693422595588&hl=en&as_sdt=0,5)]. The 3rd year review for the IAT.
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  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - A computational process-tracing method for measuring people’s planning strategies and how they change over time - ***Behavior Research Methods***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=10405935000926098041)]. Model-based strategy identification.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
  - Shared computational principles for language processing in humans and deep language models - ***Nature Neuroscience***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16078004657063602593&hl=en&as_sdt=0,5)].
  - Virtual reality in behavioral neuroscience and beyond - ***Nature Neuroscience***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12168354203281280346&hl=en&as_sdt=0,5)]. A classic review on the early applications of Virtual Reality to behavioral studies.
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  - Do People Ask Good Questions? - ***Computational Brain & Behavior***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14595996621617337270&hl=en&as_sdt=0,5)].
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- Meta-Level Considerations
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  - Balancing Constraints and Rewards with Meta-Gradient D4PG - ***ICLR'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=2805226315118298313&hl=en&as_sdt=0,5)].
  - Metacontrol for Adaptive Imagination-Based Optimization - ***ICLR'17***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=16728474512617398730&hl=en&as_sdt=0,5)].
  - On Effective Scheduling of Model-based Reinforcement Learning - ***NeurIPS'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=11128521607771619105&hl=en&as_sdt=0,5)].
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  - Gestalt Psychology - ***Psychologische Forschung***, 1967. [[All Versions](https://scholar.google.com/scholar?cluster=16023098380090751616&hl=en&as_sdt=0,5)]. Wolfgang Köhler's review on Gestalt psychology.
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  - Epiphany - ***Wikipedia***. Wikipedia on epiphany, the "feeling" when the Aha! moment comes.
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  - Dynamics and constraints in insight problem solving - ***Journal of Experimental Psychology***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12067671710370549516&hl=en&as_sdt=0,5)]. [[APA](https://psycnet.apa.org/record/2002-01361-014)].
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  - Computational rationality: A converging paradigm for intelligence in brains, minds, and machines - ***Science***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=7744057022238735461&hl=en&as_sdt=0,5)]. A comprehensive review on the rationality of Bayesian computational models.
  - Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources - ***Behavioral and Brain Sciences***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1642626865293965288&hl=en&as_sdt=0,5)]. A resource-rational account on interpreting human intelligence.
  - Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic - ***Topics in Cognitive Science***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=16305499937147933368&hl=en&as_sdt=0,5)]. An earlier version of the paper above.
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  - Epistemology - ***Plato Stanford***.
  - The secret life of predictive brains: what's spontaneous activity for? - ***Trends in Cognitive Sciences***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=719229834892860829&hl=en&as_sdt=0,5)]. A neuroscience account on brain as a generative model.
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  - Is human cognition adaptive? - ***Behavioral and Brain Sciences***, 1991. [[All Versions](https://scholar.google.com/scholar?cluster=3639936076538071052&hl=en&as_sdt=0,5)]. The original paper introducing the adaptation perspective of human intelligence, the theoretical basis of the ACT cognitive architecture.
  - Metacognition in computation: A selected research review - ***Artificial Intelligence***, 2005. [[All Versions](https://scholar.google.com/scholar?cluster=4240334051245008914&hl=en&as_sdt=0,5)].
  - Basic functional trade-offs in cognition: An integrative framework - ***Cognition***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=11475742130443069967&hl=en&as_sdt=0,5)].
  - What is consciousness, and could machines have it? - ***Science***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6932714857132107942&hl=en&as_sdt=0,5)]. A perspective on the two levels of consciousness in machine intelligence.
  - Gestalt Psychology - ***Psychologische Forschung***, 1967. [[All Versions](https://scholar.google.com/scholar?cluster=16023098380090751616&hl=en&as_sdt=0,5)]. Wolfgang Köhler's review on Gestalt psychology.
  - Gestalt Psychology - ***Psychologische Forschung***, 1967. [[All Versions](https://scholar.google.com/scholar?cluster=16023098380090751616&hl=en&as_sdt=0,5)]. Wolfgang Köhler's review on Gestalt psychology.
  - Gestalt Psychology - ***Psychologische Forschung***, 1967. [[All Versions](https://scholar.google.com/scholar?cluster=16023098380090751616&hl=en&as_sdt=0,5)]. Wolfgang Köhler's review on Gestalt psychology.
  - Gestalt Psychology - ***Psychologische Forschung***, 1967. [[All Versions](https://scholar.google.com/scholar?cluster=16023098380090751616&hl=en&as_sdt=0,5)]. Wolfgang Köhler's review on Gestalt psychology.
  - Gestalt Psychology - ***Psychologische Forschung***, 1967. [[All Versions](https://scholar.google.com/scholar?cluster=16023098380090751616&hl=en&as_sdt=0,5)]. Wolfgang Köhler's review on Gestalt psychology.
  - Gestalt Psychology - ***Psychologische Forschung***, 1967. [[All Versions](https://scholar.google.com/scholar?cluster=16023098380090751616&hl=en&as_sdt=0,5)]. Wolfgang Köhler's review on Gestalt psychology.
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  - Gestalt Psychology - ***Psychologische Forschung***, 1967. [[All Versions](https://scholar.google.com/scholar?cluster=16023098380090751616&hl=en&as_sdt=0,5)]. Wolfgang Köhler's review on Gestalt psychology.
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- Science Logology
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  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
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  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
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  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
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  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
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  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
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  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
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  - An autonomous laboratory for the accelerated synthesis of novel materials - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17944003281308189532)]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind.
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
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  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
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  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - An autonomous portable platform for universal chemical synthesis - ***Nature Chemistry***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4484997534431409967)]. [[Preprint](https://eprints.gla.ac.uk/275574/)]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control.
  - An integrated self-optimizing programmable chemical synthesis and reaction engine - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=9157508627971047184)]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - An autonomous laboratory for the accelerated synthesis of novel materials - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17944003281308189532)]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
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  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
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  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
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  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
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  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
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  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
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  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
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  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
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  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - Bridging the information gap in organic chemical reactions - ***Nature Chemistry***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=5365091261196953334)]. This perspective article formulates eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards.
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  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Five keys to writing a reproducible lab protocol - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=13259206850261301938)]. This interviewing paper introduces five ways to increase the reproducibility of experimental protocols: (i) documenting protocols as the experiment goes; (ii) providing video illustrations in addition to written protocols; (iii) using electronic lab notebooks (ELNs) for managing experimental resources digitally; (iv) depositing and documenting reagents with understanding the rationale behind every step; and (v) exploiting online platforms to share tips, extensions, methods, and data among researchers.
  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - An autonomous portable platform for universal chemical synthesis - ***Nature Chemistry***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4484997534431409967)]. [[Preprint](https://eprints.gla.ac.uk/275574/)]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control.
  - An integrated self-optimizing programmable chemical synthesis and reaction engine - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=9157508627971047184)]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
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  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
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  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Structured information extraction from scientific text with large language models - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=13694008040033857249)]. This paper presents a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models can be fine-tuned to extract useful records of complex scientific knowledge. The authors test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction.
  - Bridging the information gap in organic chemical reactions - ***Nature Chemistry***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=5365091261196953334)]. This perspective article formulates eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards.
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Structured information extraction from scientific text with large language models - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=13694008040033857249)]. This paper presents a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models can be fine-tuned to extract useful records of complex scientific knowledge. The authors test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction.
  - Automated extraction of chemical synthesis actions from experimental procedures - ***Nature Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1626689948540815082)]. This paper presents a method to convert unstructured experimental procedures written in English to structured synthetic steps (action sequences) reflecting all the operations needed to successfully conduct the corresponding chemical reactions.
  - Inferring experimental procedures from text-based representations of chemical reactions - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=15772647675166217556)]. This paper presents data-driven models for predicting the entire sequence of synthesis steps starting from a textual representation of a chemical equation, for application in batch organic chemistry.
  - Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis - ***Nature Communications***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8186755371438552520)]. This paper introduces a transformer model for automated synthesis protocol analysis in catalyst discovery, exemplified using single-atom heterogeneous catalysts (SACs), a rapidly expanding catalyst family. The model adeptly converts SAC protocols into action sequences, and this output is used to facilitate statistical inference of their synthesis trends and applications, potentially expediting literature review and analysis.
  - Bridging the information gap in organic chemical reactions - ***Nature Chemistry***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=5365091261196953334)]. This perspective article formulates eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards.
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Five keys to writing a reproducible lab protocol - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=13259206850261301938)]. This interviewing paper introduces five ways to increase the reproducibility of experimental protocols: (i) documenting protocols as the experiment goes; (ii) providing video illustrations in addition to written protocols; (iii) using electronic lab notebooks (ELNs) for managing experimental resources digitally; (iv) depositing and documenting reagents with understanding the rationale behind every step; and (v) exploiting online platforms to share tips, extensions, methods, and data among researchers.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - An autonomous portable platform for universal chemical synthesis - ***Nature Chemistry***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4484997534431409967)]. [[Preprint](https://eprints.gla.ac.uk/275574/)]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control.
  - An integrated self-optimizing programmable chemical synthesis and reaction engine - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=9157508627971047184)]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - An autonomous laboratory for the accelerated synthesis of novel materials - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17944003281308189532)]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Optimizing Spaced Repetition Schedule by Capturing the Dynamics of Memory - ***IEEE Transactions on Knowledge and Data Engineering***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=949715967083833369&hl=en&as_sdt=0,10)].
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Structured information extraction from scientific text with large language models - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=13694008040033857249)]. This paper presents a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models can be fine-tuned to extract useful records of complex scientific knowledge. The authors test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction.
  - Automated extraction of chemical synthesis actions from experimental procedures - ***Nature Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1626689948540815082)]. This paper presents a method to convert unstructured experimental procedures written in English to structured synthetic steps (action sequences) reflecting all the operations needed to successfully conduct the corresponding chemical reactions.
  - Inferring experimental procedures from text-based representations of chemical reactions - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=15772647675166217556)]. This paper presents data-driven models for predicting the entire sequence of synthesis steps starting from a textual representation of a chemical equation, for application in batch organic chemistry.
  - Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis - ***Nature Communications***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8186755371438552520)]. This paper introduces a transformer model for automated synthesis protocol analysis in catalyst discovery, exemplified using single-atom heterogeneous catalysts (SACs), a rapidly expanding catalyst family. The model adeptly converts SAC protocols into action sequences, and this output is used to facilitate statistical inference of their synthesis trends and applications, potentially expediting literature review and analysis.
  - Bridging the information gap in organic chemical reactions - ***Nature Chemistry***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=5365091261196953334)]. This perspective article formulates eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards.
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Five keys to writing a reproducible lab protocol - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=13259206850261301938)]. This interviewing paper introduces five ways to increase the reproducibility of experimental protocols: (i) documenting protocols as the experiment goes; (ii) providing video illustrations in addition to written protocols; (iii) using electronic lab notebooks (ELNs) for managing experimental resources digitally; (iv) depositing and documenting reagents with understanding the rationale behind every step; and (v) exploiting online platforms to share tips, extensions, methods, and data among researchers.
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - An autonomous portable platform for universal chemical synthesis - ***Nature Chemistry***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4484997534431409967)]. [[Preprint](https://eprints.gla.ac.uk/275574/)]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control.
  - An integrated self-optimizing programmable chemical synthesis and reaction engine - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=9157508627971047184)]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - An autonomous laboratory for the accelerated synthesis of novel materials - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17944003281308189532)]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind.
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
  - A dynamic knowledge graph approach to distributed self-driving laboratories - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=7070798385652764751)]. This work employs ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. The architecture is built upon the World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - An autonomous portable platform for universal chemical synthesis - ***Nature Chemistry***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4484997534431409967)]. [[Preprint](https://eprints.gla.ac.uk/275574/)]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control.
  - An integrated self-optimizing programmable chemical synthesis and reaction engine - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=9157508627971047184)]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - An autonomous laboratory for the accelerated synthesis of novel materials - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17944003281308189532)]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind.
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
  - Structured information extraction from scientific text with large language models - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=13694008040033857249)]. This paper presents a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models can be fine-tuned to extract useful records of complex scientific knowledge. The authors test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction.
  - Automated extraction of chemical synthesis actions from experimental procedures - ***Nature Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1626689948540815082)]. This paper presents a method to convert unstructured experimental procedures written in English to structured synthetic steps (action sequences) reflecting all the operations needed to successfully conduct the corresponding chemical reactions.
  - Inferring experimental procedures from text-based representations of chemical reactions - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=15772647675166217556)]. This paper presents data-driven models for predicting the entire sequence of synthesis steps starting from a textual representation of a chemical equation, for application in batch organic chemistry.
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis - ***Nature Communications***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8186755371438552520)]. This paper introduces a transformer model for automated synthesis protocol analysis in catalyst discovery, exemplified using single-atom heterogeneous catalysts (SACs), a rapidly expanding catalyst family. The model adeptly converts SAC protocols into action sequences, and this output is used to facilitate statistical inference of their synthesis trends and applications, potentially expediting literature review and analysis.
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Bridging the information gap in organic chemical reactions - ***Nature Chemistry***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=5365091261196953334)]. This perspective article formulates eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards.
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Five keys to writing a reproducible lab protocol - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=13259206850261301938)]. This interviewing paper introduces five ways to increase the reproducibility of experimental protocols: (i) documenting protocols as the experiment goes; (ii) providing video illustrations in addition to written protocols; (iii) using electronic lab notebooks (ELNs) for managing experimental resources digitally; (iv) depositing and documenting reagents with understanding the rationale behind every step; and (v) exploiting online platforms to share tips, extensions, methods, and data among researchers.
  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - An autonomous portable platform for universal chemical synthesis - ***Nature Chemistry***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4484997534431409967)]. [[Preprint](https://eprints.gla.ac.uk/275574/)]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control.
  - An integrated self-optimizing programmable chemical synthesis and reaction engine - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=9157508627971047184)]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - An autonomous laboratory for the accelerated synthesis of novel materials - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17944003281308189532)]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind.
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
  - A dynamic knowledge graph approach to distributed self-driving laboratories - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=7070798385652764751)]. This work employs ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. The architecture is built upon the World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4 - ***Microsoft Research AI4Science***, 2023. [[All Versions]()]. [[Project](https://github.com/microsoft/LLM4ScientificDiscovery)]. A survey on the performance of LLMs within the context of scientific discovery, focusing on GPT-4.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge - ***Journal of the American Chemical Society***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=10595951443492961310)]. The development of AI synthesis planners trained solely on reaction-example-data has stagnated and is not on par with the performance of “hybrid” algorithms combining AI with expert knowledge. This Perspective examines possible causes of these shortcomings, extending beyond the established reasoning of insufficient quantities of reaction data. The authors advocate augmenting the unique capabilities of AI with the knowledge base and the reasoning strategies of domain experts.
  - Bridging the information gap in organic chemical reactions - ***Nature Chemistry***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=5365091261196953334)]. This perspective article formulates eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards.
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Five keys to writing a reproducible lab protocol - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=13259206850261301938)]. This interviewing paper introduces five ways to increase the reproducibility of experimental protocols: (i) documenting protocols as the experiment goes; (ii) providing video illustrations in addition to written protocols; (iii) using electronic lab notebooks (ELNs) for managing experimental resources digitally; (iv) depositing and documenting reagents with understanding the rationale behind every step; and (v) exploiting online platforms to share tips, extensions, methods, and data among researchers.
  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - An autonomous portable platform for universal chemical synthesis - ***Nature Chemistry***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4484997534431409967)]. [[Preprint](https://eprints.gla.ac.uk/275574/)]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control.
  - An integrated self-optimizing programmable chemical synthesis and reaction engine - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=9157508627971047184)]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - An autonomous laboratory for the accelerated synthesis of novel materials - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17944003281308189532)]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind.
  - Automation isn't automatic - ***Chemical Science***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=14176714971050097971)]. This perspective provides an overview of the current state of automation of synthetic chemistry at the benchtop scale with a particular emphasis on core considerations and the ensuing challenges of deploying a system. The authors aim to reframe automation as decidedly not automatic but rather an iterative process that involves a series of careful decisions (both human and computational) and constant adjustment.
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
  - Balancing act: when to flex and when to stay fixed - 6) - ***Trends in Chemistry***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=14208571639305934551)]. This perspective article provides essential insights into the decision-making process for choosing automation platforms, highlighting the suitability of fixed automation for standardized tasks and the strategic use of flexible automation in dynamic research settings.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Digitization and validation of a chemical synthesis literature database in the ChemPU - ***Science***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17368503277308594977)]. [[Preprint](https://www.researchgate.net/profile/Aamir-Khan/publication/361857872_Digitization_and_validation_of_a_chemical_synthesis_literature_database_in_the_ChemPU/links/62cd356d00d0b451104cbfe9/Digitization-and-validation-of-a-chemical-synthesis-literature-database-in-the-ChemPU.pdf)]. This paper presents an automatically executable chemical reaction database of 100 molecules representative of the range of reactions found in contemporary organic synthesis. The chemical reaction codes or χDLs for the reactions have been stored in a database for version control, validation, collaboration, and data mining. Of these syntheses, more than 50 entries from the database have been downloaded and robotically run in seven modular chemputers with yields and purities comparable to those achieved by an expert chemist.
  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - An integrated self-optimizing programmable chemical synthesis and reaction engine - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=9157508627971047184)]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - Organic synthesis in a modular robotic system driven by a chemical programming language - ***Science***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13920677955690815682&hl=en&as_sdt=0,5)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/387-Steiner-ScienceJan19.full.pdf)]. [[Perspective: Democratizing synthesis by automation](https://www.science.org/doi/10.1126/science.aav8816)]. This paper develops an autonomous compiler and robotic laboratory platform to synthesize organic compounds on the basis of standardized methods descriptions. The platform comprises conventional equipment such as round-bottom flasks, separatory funnels, and a rotary evaporator to maximize its compatibility with extant literature. The authors showcase the system with short syntheses of three common pharmaceuticals that proceeded comparably to manual synthesis.
  - A universal system for digitization and automatic execution of the chemical synthesis literature - ***Science***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13909991218383718512&hl=en&as_sdt=0,5)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/Mehr-ScienceOct2020.pdf)]. [[XDL Documentation](https://croningroup.gitlab.io/chemputer/xdl/index.html)]. [[XDL Schema Database](https://zenodo.org/records/3955107)]. This paper reports a software platform that uses natural language processing to translate the organic chemistry literature directly into editable code, which in turn can be compiled to drive automated synthesis of the compound in the laboratory.
  - Chemputation and the Standardization of Chemical Informatics - ***Journal of the American Chemical Society (Au)***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=3884902150148113559)]. This paper describes a standard hardware (the chemical processing programming architecture --- the ChemPU) to encompass all chemical synthesis, an approach which unifies all chemistry automation strategies, from solid-phase peptide synthesis, to HTE flow chemistry platforms, while at the same time establishing a publication standard so that researchers can exchange chemical code (χDL) to ensure reproducibility and interoperability.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - Structured information extraction from scientific text with large language models - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=13694008040033857249)]. This paper presents a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models can be fine-tuned to extract useful records of complex scientific knowledge. The authors test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction.
  - Automated extraction of chemical synthesis actions from experimental procedures - ***Nature Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1626689948540815082)]. This paper presents a method to convert unstructured experimental procedures written in English to structured synthetic steps (action sequences) reflecting all the operations needed to successfully conduct the corresponding chemical reactions.
  - Inferring experimental procedures from text-based representations of chemical reactions - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=15772647675166217556)]. This paper presents data-driven models for predicting the entire sequence of synthesis steps starting from a textual representation of a chemical equation, for application in batch organic chemistry.
  - Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis - ***Nature Communications***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8186755371438552520)]. This paper introduces a transformer model for automated synthesis protocol analysis in catalyst discovery, exemplified using single-atom heterogeneous catalysts (SACs), a rapidly expanding catalyst family. The model adeptly converts SAC protocols into action sequences, and this output is used to facilitate statistical inference of their synthesis trends and applications, potentially expediting literature review and analysis.
  - Bridging the information gap in organic chemical reactions - ***Nature Chemistry***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=5365091261196953334)]. This perspective article formulates eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards.
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Five keys to writing a reproducible lab protocol - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=13259206850261301938)]. This interviewing paper introduces five ways to increase the reproducibility of experimental protocols: (i) documenting protocols as the experiment goes; (ii) providing video illustrations in addition to written protocols; (iii) using electronic lab notebooks (ELNs) for managing experimental resources digitally; (iv) depositing and documenting reagents with understanding the rationale behind every step; and (v) exploiting online platforms to share tips, extensions, methods, and data among researchers.
  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - An autonomous portable platform for universal chemical synthesis - ***Nature Chemistry***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4484997534431409967)]. [[Preprint](https://eprints.gla.ac.uk/275574/)]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control.
  - An integrated self-optimizing programmable chemical synthesis and reaction engine - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=9157508627971047184)]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - An autonomous laboratory for the accelerated synthesis of novel materials - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17944003281308189532)]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind.
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
  - A dynamic knowledge graph approach to distributed self-driving laboratories - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=7070798385652764751)]. This work employs ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. The architecture is built upon the World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - An autonomous portable platform for universal chemical synthesis - ***Nature Chemistry***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4484997534431409967)]. [[Preprint](https://eprints.gla.ac.uk/275574/)]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control.
  - An integrated self-optimizing programmable chemical synthesis and reaction engine - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=9157508627971047184)]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - An autonomous laboratory for the accelerated synthesis of novel materials - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17944003281308189532)]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind.
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
  - Bridging the information gap in organic chemical reactions - ***Nature Chemistry***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=5365091261196953334)]. This perspective article formulates eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards.
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Five keys to writing a reproducible lab protocol - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=13259206850261301938)]. This interviewing paper introduces five ways to increase the reproducibility of experimental protocols: (i) documenting protocols as the experiment goes; (ii) providing video illustrations in addition to written protocols; (iii) using electronic lab notebooks (ELNs) for managing experimental resources digitally; (iv) depositing and documenting reagents with understanding the rationale behind every step; and (v) exploiting online platforms to share tips, extensions, methods, and data among researchers.
  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - An autonomous portable platform for universal chemical synthesis - ***Nature Chemistry***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4484997534431409967)]. [[Preprint](https://eprints.gla.ac.uk/275574/)]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - An integrated self-optimizing programmable chemical synthesis and reaction engine - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=9157508627971047184)]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - An autonomous laboratory for the accelerated synthesis of novel materials - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17944003281308189532)]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind.
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - An autonomous portable platform for universal chemical synthesis - ***Nature Chemistry***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4484997534431409967)]. [[Preprint](https://eprints.gla.ac.uk/275574/)]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control.
  - An integrated self-optimizing programmable chemical synthesis and reaction engine - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=9157508627971047184)]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - An autonomous laboratory for the accelerated synthesis of novel materials - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17944003281308189532)]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind.
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Reconfigurable system for automated optimization of diverse chemical reactions - ***Science***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=3076614068291119943)]. [[Preprint](https://www.science.org/doi/pdf/10.1126/science.aat0650)]. This paper describes a plug-and-play, continuous-flow chemical synthesis system that mitigates this challenge with an integrated combination of hardware, software, and analytics. The system software controls the user-selected reagents and unit operations (reactors and separators), processes reaction analytics (high-performance liquid chromatography, mass spectrometry, vibrational spectroscopy), and conducts automated optimizations.
  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - An autonomous portable platform for universal chemical synthesis - ***Nature Chemistry***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4484997534431409967)]. [[Preprint](https://eprints.gla.ac.uk/275574/)]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control.
  - An integrated self-optimizing programmable chemical synthesis and reaction engine - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=9157508627971047184)]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - An autonomous laboratory for the accelerated synthesis of novel materials - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17944003281308189532)]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind.
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
  - What is a minimal working example for a self-driving laboratory? - ***Matter***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=1612804023616680548)]. This paper proposes SDL-Demo: a low-cost “Hello, World!” for self-driving laboratories that combines “Hello, World!” tasks from electronics, physics-based simulations, and optimization. SDL-Demo is modular and extensible, making it an ideal candidate for low-cost teaching and prototyping of self-driving laboratory concepts.
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Five keys to writing a reproducible lab protocol - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=13259206850261301938)]. This interviewing paper introduces five ways to increase the reproducibility of experimental protocols: (i) documenting protocols as the experiment goes; (ii) providing video illustrations in addition to written protocols; (iii) using electronic lab notebooks (ELNs) for managing experimental resources digitally; (iv) depositing and documenting reagents with understanding the rationale behind every step; and (v) exploiting online platforms to share tips, extensions, methods, and data among researchers.
  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - An autonomous portable platform for universal chemical synthesis - ***Nature Chemistry***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4484997534431409967)]. [[Preprint](https://eprints.gla.ac.uk/275574/)]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control.
  - An integrated self-optimizing programmable chemical synthesis and reaction engine - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=9157508627971047184)]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.
  - Bridging the information gap in organic chemical reactions - ***Nature Chemistry***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=5365091261196953334)]. This perspective article formulates eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - An autonomous laboratory for the accelerated synthesis of novel materials - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17944003281308189532)]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind.
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - Bridging the information gap in organic chemical reactions - ***Nature Chemistry***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=5365091261196953334)]. This perspective article formulates eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards.
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Five keys to writing a reproducible lab protocol - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=13259206850261301938)]. This interviewing paper introduces five ways to increase the reproducibility of experimental protocols: (i) documenting protocols as the experiment goes; (ii) providing video illustrations in addition to written protocols; (iii) using electronic lab notebooks (ELNs) for managing experimental resources digitally; (iv) depositing and documenting reagents with understanding the rationale behind every step; and (v) exploiting online platforms to share tips, extensions, methods, and data among researchers.
  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - An autonomous laboratory for the accelerated synthesis of novel materials - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17944003281308189532)]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind.
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - A foundation model for generalizable disease detection from retinal images - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3139988207343394501&hl=en&as_sdt=0,5)].
  - Accurate medium-range global weather forecasting with 3D neural networks - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=7198604620204619820&hl=en&as_sdt=0,5)].
  - Skilful nowcasting of extreme precipitation with NowcastNet - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17837864391812838009&hl=en&as_sdt=0,5)].
  - Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=6593978922251447907&hl=en&as_sdt=0,5)].
  - Comparability of automated human induced pluripotent stem cell culture: a pilot study - ***Bioprocess and Biosystems Engineering***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=14666375402220991095&hl=en&as_sdt=0,5)].
  - An autonomous portable platform for universal chemical synthesis - ***Nature Chemistry***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4484997534431409967)]. [[Preprint](https://eprints.gla.ac.uk/275574/)]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control.
  - An integrated self-optimizing programmable chemical synthesis and reaction engine - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=9157508627971047184)]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.
  - Structured information extraction from scientific text with large language models - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=13694008040033857249)]. This paper presents a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models can be fine-tuned to extract useful records of complex scientific knowledge. The authors test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction.
  - Bridging the information gap in organic chemical reactions - ***Nature Chemistry***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=5365091261196953334)]. This perspective article formulates eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards.
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Five keys to writing a reproducible lab protocol - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=13259206850261301938)]. This interviewing paper introduces five ways to increase the reproducibility of experimental protocols: (i) documenting protocols as the experiment goes; (ii) providing video illustrations in addition to written protocols; (iii) using electronic lab notebooks (ELNs) for managing experimental resources digitally; (iv) depositing and documenting reagents with understanding the rationale behind every step; and (v) exploiting online platforms to share tips, extensions, methods, and data among researchers.
  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - An autonomous portable platform for universal chemical synthesis - ***Nature Chemistry***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4484997534431409967)]. [[Preprint](https://eprints.gla.ac.uk/275574/)]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control.
  - A mobile robotic chemist - ***Nature***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13216902493789027324&hl=en&as_sdt=0,5)]. [[Preprint](https://strathprints.strath.ac.uk/74759/1/Burger_etal_Nature_2020_A_mobile_robotic.pdf)]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.
  - An autonomous laboratory for the accelerated synthesis of novel materials - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=17944003281308189532)]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind.
  - Autonomous chemical research with large language models - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8097577445064259203&hl=en&as_sdt=0,5)]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.
  - The Internet of Things comes to the lab - ***Nature***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7747117198956166976&hl=en&as_sdt=0,5)]. The emergence of connected instruments and equipment promises to untether researchers from the laboratory --- letting them fine-tune experiments and analyse data remotely.
  - Scientific discovery in the age of artificial intelligence - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11962817646389491592&hl=en&as_sdt=0,5)]. A review article that examines breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency.
  - Human–machine collaboration for improving semiconductor process development - ***Nature***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10295771969614897767&hl=en&as_sdt=0,5)]. [[Nature News](https://www.nature.com/articles/d41586-023-01353-x)].
  - An integrated self-optimizing programmable chemical synthesis and reaction engine - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=9157508627971047184)]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.
  - Structured information extraction from scientific text with large language models - ***Nature Communications***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=13694008040033857249)]. This paper presents a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models can be fine-tuned to extract useful records of complex scientific knowledge. The authors test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction.
  - Automated extraction of chemical synthesis actions from experimental procedures - ***Nature Communications***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1626689948540815082)]. This paper presents a method to convert unstructured experimental procedures written in English to structured synthetic steps (action sequences) reflecting all the operations needed to successfully conduct the corresponding chemical reactions.
  - Inferring experimental procedures from text-based representations of chemical reactions - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=15772647675166217556)]. This paper presents data-driven models for predicting the entire sequence of synthesis steps starting from a textual representation of a chemical equation, for application in batch organic chemistry.
  - Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis - ***Nature Communications***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8186755371438552520)]. This paper introduces a transformer model for automated synthesis protocol analysis in catalyst discovery, exemplified using single-atom heterogeneous catalysts (SACs), a rapidly expanding catalyst family. The model adeptly converts SAC protocols into action sequences, and this output is used to facilitate statistical inference of their synthesis trends and applications, potentially expediting literature review and analysis.
  - Bridging the information gap in organic chemical reactions - ***Nature Chemistry***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=5365091261196953334)]. This perspective article formulates eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards.
  - A manifesto for reproducible science - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9515807942859203900&hl=en&as_sdt=0,10)].
  - 1,500 scientists lift the lid on reproducibility - ***Nature***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11479406257389837824&hl=en&as_sdt=0,5)].
  - Six factors affecting reproducibility in life science research and how to handle them - ***Nature Advertisement***.
  - Five keys to writing a reproducible lab protocol - ***Nature***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=13259206850261301938)]. This interviewing paper introduces five ways to increase the reproducibility of experimental protocols: (i) documenting protocols as the experiment goes; (ii) providing video illustrations in addition to written protocols; (iii) using electronic lab notebooks (ELNs) for managing experimental resources digitally; (iv) depositing and documenting reagents with understanding the rationale behind every step; and (v) exploiting online platforms to share tips, extensions, methods, and data among researchers.
  - Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine - ***Nature Chemistry***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=18024303106901939347)]. [[Preprint](https://eprints.gla.ac.uk/231947/)]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.
  - An autonomous portable platform for universal chemical synthesis - ***Nature Chemistry***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4484997534431409967)]. [[Preprint](https://eprints.gla.ac.uk/275574/)]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform a protocol quality control.
- Theory of Mind
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  - Theory of Mind - ***Wikipedia***. Wikipedia on Theory of Mind (ToM), a cognitive capability that estimating others' goal, belief, and desire.
  - Cognitive Science - ***Plato Stanford***.
  - Intentionality - ***Plato Stanford***.
  - The Mind/Brain Identity Theory - ***Plato Stanford***.
  - Mental Representation - ***Plato Stanford***.
  - Mental Imagery - ***Plato Stanford***.
  - Temporal Consciousness - ***Plato Stanford***.
  - The Experience and Perception of Time - ***Plato Stanford***.
  - Practical Reason - ***Plato Stanford***.
  - Memory - ***Plato Stanford***.-->
  - The Computational Theory of Mind - ***Plato Stanford***. A computational philosophy account on ToM. -->
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  - Planning with theory of mind - ***Trends in Cognitive Sciences***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=8461125353366208047&hl=en&as_sdt=0,5)]. A perspective on understanding Theory of Mind through planning that consists of abstract structured causal representations and supports efficient search and selection from innumerable possible actions.
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  - Bayesian Theory of Mind: Modeling Joint Belief-Desire Attribution - ***CogSci'11***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7454981153033683025&hl=en&as_sdt=0,5)].
  - Bayesian Theory of Mind : modeling human reasoning about beliefs, desires, goals, and social relations - ***Ph.D. Dissertation MIT***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=16053170661075048224&hl=en&as_sdt=0,5)]. Chris Baker's Ph.D. dissertation, a comprehensive review on Bayesian modeling of Theory of Mind.
  - Bayesian Brains without Probabilities - ***Trends in Cognitive Sciences***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=13076510377612067772&hl=en&as_sdt=0,5)]. A perspective on human probabilistic modeling without explicit probabilistic computation.
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - The Bayesian Brain: An Introduction to Predictive Processing - 2018.
  - Machine theory of mind - ***ICML'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=6267278380616425333&hl=en&as_sdt=0,5)].
  - Theory of mind as inverse reinforcement learning - ***Current Opinion in Behavioral Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=14959443239271810913&hl=en&as_sdt=0,5)].
  - Computational Models of Emotion Inference in Theory of Mind: A Review and Roadmap - ***Topics in Cognitive Science***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15919410726494658168&hl=en&as_sdt=0,5)].
  - The Naïve Utility Calculus as a unified, quantitative framework for action understanding - ***Cognitive Psychology***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=10366690800692546587&hl=en&as_sdt=0,5)]. [[Project](http://www.github.com/julianje/bishop)].
  - AGENT: A Benchmark for Core Psychological Reasoning - ***ICML'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=9729067071974484204&hl=en&as_sdt=0,5)]. A benchmark for AI that modeling the core knowledge of ToM.
  - Experimental Games and Social Decision Making - ***Annual Review of Psychology***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=4713510112126264116&hl=en&as_sdt=0,5)]. A comprehensive review on social ToM experiment pafadigms.
  - Theory of Minds: Understanding Behavior in Groups through Inverse Planning - ***AAAI'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6755247312077985817&hl=en&as_sdt=0,5)]. Inverse Planning in multi-agent setting.
  - Leveraging Facial Expressions and Contextual Information to Investigate Opaque Representations of Emotion - ***Emotion***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=9634378462684744548&hl=en&as_sdt=0,5)].
  - Waiting and weighting: Information sampling is a balance between efficiency and error-reduction - ***Cognition***, 2013. [[All Versions](https://scholar.google.com/scholar?cluster=12787722822882067638&hl=en&as_sdt=0,5)].
  - Natural scene statistics account for the representation of scene categories in human visual cortex - ***Neuron***, 2013. [[All Versions](https://scholar.google.com/scholar?cluster=14030885492052338412&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Unit of visual working memory: A Boolean map provides a better account than an object does - ***Journal of Experimental Psychology***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=14909735035752892020&hl=en&as_sdt=0,5)].
  - The logic of universalization guides moral judgment - ***Proceedings of the National Academy of Sciences***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13482051983012049752&hl=en&as_sdt=0,5)].
  - Learning Triadic Belief Dynamics in Nonverbal Communication from Videos - ***CVPR'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=15365483338824697316&hl=en&as_sdt=0,5)]. Theory of Mind in the perception level, introduced as a computer vision task.
  - Ten-month-old infants infer the value of goals from the costs of actions - ***Science***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=11862940312128630925&hl=en&as_sdt=0,5)]. A piece of evidence for children's capability on ToM.
  - Origins of the concepts cause, cost, and goal in prereaching infants - ***Proceedings of the National Academy of Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15973074852436355789&hl=en&as_sdt=0,5)].
  - Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others - ***NeurIPS'21***, 2021. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=16514364601966350574)].
  - Intentonomy: a Dataset and Study towards Human Intent Understanding - ***CVPR'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=5268870345003195142&hl=en&as_sdt=0,5)]. A large-scale database on human intentionally-posted images on social media.
  - Adventures in Flatland: Perceiving Social Interactions Under Physical Dynamics - ***CogSci'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1928005249823745390&hl=en&as_sdt=0,5)].
  - PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception - ***AAAI'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=15536873427310696150&hl=en&as_sdt=0,5)]. [[Project](https://tshu.io/PHASE/)].
  - Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration - ***ICLR'21***, 2021. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=16340001407726295133)].
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  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - The Signature of All Things: Children Infer Knowledge States from Static Images - ***CogSci'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=12380982112592086477&hl=en&as_sdt=0,5&as_ylo=2017)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Experimental Games and Social Decision Making - ***Annual Review of Psychology***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=4713510112126264116&hl=en&as_sdt=0,5)]. A comprehensive review on social ToM experiment pafadigms.
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Experimental Games and Social Decision Making - ***Annual Review of Psychology***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=4713510112126264116&hl=en&as_sdt=0,5)]. A comprehensive review on social ToM experiment pafadigms.
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Experimental Games and Social Decision Making - ***Annual Review of Psychology***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=4713510112126264116&hl=en&as_sdt=0,5)]. A comprehensive review on social ToM experiment pafadigms.
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
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  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - The Bayesian Brain: An Introduction to Predictive Processing - 2018.
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Experimental Games and Social Decision Making - ***Annual Review of Psychology***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=4713510112126264116&hl=en&as_sdt=0,5)]. A comprehensive review on social ToM experiment pafadigms.
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - The Bayesian Brain: An Introduction to Predictive Processing - 2018.
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Experimental Games and Social Decision Making - ***Annual Review of Psychology***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=4713510112126264116&hl=en&as_sdt=0,5)]. A comprehensive review on social ToM experiment pafadigms.
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Experimental Games and Social Decision Making - ***Annual Review of Psychology***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=4713510112126264116&hl=en&as_sdt=0,5)]. A comprehensive review on social ToM experiment pafadigms.
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
  - Rational quantitative attribution of beliefs, desires and percepts in human mentalizing - ***Nature Human Behavior***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=9377509910551057835&hl=en&as_sdt=0,5)].
  - Using human brain activity to guide machine learning - ***Scientific Report***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=12987955253653036948&hl=en&as_sdt=0,5)].
- Commonsense
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - From Recognition to Cognition: Visual Commonsense Reasoning - ***CVPR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15467433880059136365&hl=en&as_sdt=0,5)]. [[Project](http://visualcommonsense.com/)].
  - Visual Commonsense R-CNN - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6886229776034162585&hl=en&as_sdt=0,5)].
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive Physics: Current Research and Controversies - ***Trends in Cognitive Sciences***, 2018. [[All Versions](https://scholar.google.com/scholar?start=0&hl=en&as_sdt=0,5&cluster=12085981794958916203)]. Hongjing Lu's review on intuitive physics.
  - Simulation as an engine of physical scene understanding - ***Proceedings of the National Academy of Sciences***, 2013. [[All Versions](https://scholar.google.com/scholar?cluster=5892822406285231676&hl=en&as_sdt=0,5)]. [[Appendix](https://www.pnas.org/content/pnas/suppl/2013/10/18/1306572110.DCSupplemental/pnas.201306572SI.pdf?targetid=nameddest%3DSTXT)]. The first attempt to computationally simulate intuitive physics.
  - Functional neuroanatomy of intuitive physical inference - ***Proceedings of the National Academy of Sciences***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=1792195093536891402&hl=en&as_sdt=0,5)]. A piece of evidence for the functional part of intuitive physics in human brain.
  - Mind Games: Game Engines as an Architecture for Intuitive Physics - ***Trends in Cognitive Sciences***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=14527964477161848029&hl=en&as_sdt=2005&sciodt=0,5)]. Tomer Ullman's review on simulation-based intuitive physics.
  - Learning physical parameters from dynamic scenes - ***Cognitive Psychology***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=5103729321433959736&hl=en&as_sdt=0,5)].
  - Limits on Simulation Approaches in Intuitive Physics - ***Cognitive Psychology***, 2021. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=6329029167380621767)]. Ernest Davis's perspective against intuitive physics, that physcial reasoning is logical reasoning instead of intuition.
  - Partial Mental Simulation Explains Fallacies in Physical Reasoning - ***Cognitive Neuropsychology***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=15541954459060383152&hl=en&as_sdt=2005)].
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - PHYRE: A New Benchmark for Physical Reasoning - ***NeurIPS'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=9555658528231205655&hl=en&as_sdt=0,5)]. A benchmark for AI physical reasoning.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Representations of Commonsense Knowledge - ***Morgan Kaufmann***, 1990. [[All Versions](https://scholar.google.com/scholar?cluster=8861902735724600978&hl=en&as_sdt=0,5)]. A classic book on commonsense knowledge.
  - Towards a theory of commonsense visual reasoning - ***FSTTCS***, 1990. [[All Versions](https://scholar.google.com/scholar?cluster=13178231862265713961&hl=en&as_sdt=0,5)]. The original paper on visual commonsense.
  - Commonsense reasoning and commonsense knowledge in artificial intelligence - ***Communications of the ACM***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=13786590180441485203&hl=en&as_sdt=0,5)]. Gary Marcus's review on commonsense knowledge in AI.
  - From Recognition to Cognition: Visual Commonsense Reasoning - ***CVPR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15467433880059136365&hl=en&as_sdt=0,5)]. [[Project](http://visualcommonsense.com/)].
  - PIQA: Reasoning about Physical Commonsense in Natural Language - ***AAAI'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=10110424163152713144&hl=en&as_sdt=0,5)].
  - Visual Commonsense R-CNN - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6886229776034162585&hl=en&as_sdt=0,5)].
  - Abductive Commonsense Reasoning - ***ICLR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=16544200144479839958&hl=en&as_sdt=0,5)]. Abductive commonsense reasoning on large language models.
  - VisualCOMET: Reasoning About the Dynamic Context of a Still Image - ***ECCV'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7681600847940772451&hl=en&as_sdt=0,5)].
  - The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning - 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=18355807581692234364)].
  - Experience Grounds Language - ***EMNLP'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=3734668471751920487&hl=en&as_sdt=0,5)]. A perspective on the furture of computational linguistics research---commonsense-driven and embodied language.
  - Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning - ***EMNLP'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12305856131717604775&hl=en&as_sdt=0,5)].
  - Human-like property induction is a challenge for large language models - ***CogSci'22***, 2022.
  - CYC: A Large-Scale Investment in Knowledge Infrastructure - ***Communications of the ACM***, 1995. [[All Versions](https://scholar.google.com/scholar?cluster=6505009388871605141&hl=en&as_sdt=0,5)]. The first attempt to build large-scale commonse knoweldgebase from human knowledge.
  - ConceptNet 5.5: An Open Multilingual Graph of General Knowledge - ***AAAI'17***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=7089916805257737701&hl=en&as_sdt=0,5)]. Latest version of ConceptNet.
  - The Public Acquisition of Commonsense Knowledge - ***Proceedings of AAAI Spring Symposium on Acquiring (and Using) Linguistic (and World) Knowledge for Information Access***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=12533779219524472080&hl=en&as_sdt=0,5)]. The first attempt for acquring commonsense knowlege from humans' activities on the internet.
  - Verbosity: A Game for Collecting Common-Sense Facts - ***CHI'06***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=7793704394155465847&hl=en&as_sdt=0,5)].
  - Designing games with a purpose - ***Communications of the ACM***, 2008. [[All Versions](https://scholar.google.com/scholar?cluster=18332117920150730595&hl=en&as_sdt=2005&sciodt=0,5)].
  - Acquiring Comparative Commonsense Knowledge from the Web - ***AAAI'14***, 2014. [[All Versions](https://scholar.google.com/scholar?cluster=16641273554706459553&hl=en&as_sdt=0,5)].
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - From Recognition to Cognition: Visual Commonsense Reasoning - ***CVPR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15467433880059136365&hl=en&as_sdt=0,5)]. [[Project](http://visualcommonsense.com/)].
  - Visual Commonsense R-CNN - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6886229776034162585&hl=en&as_sdt=0,5)].
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - From Recognition to Cognition: Visual Commonsense Reasoning - ***CVPR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15467433880059136365&hl=en&as_sdt=0,5)]. [[Project](http://visualcommonsense.com/)].
  - Visual Commonsense R-CNN - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6886229776034162585&hl=en&as_sdt=0,5)].
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - From Recognition to Cognition: Visual Commonsense Reasoning - ***CVPR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15467433880059136365&hl=en&as_sdt=0,5)]. [[Project](http://visualcommonsense.com/)].
  - Visual Commonsense R-CNN - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6886229776034162585&hl=en&as_sdt=0,5)].
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - From Recognition to Cognition: Visual Commonsense Reasoning - ***CVPR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15467433880059136365&hl=en&as_sdt=0,5)]. [[Project](http://visualcommonsense.com/)].
  - Visual Commonsense R-CNN - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6886229776034162585&hl=en&as_sdt=0,5)].
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - From Recognition to Cognition: Visual Commonsense Reasoning - ***CVPR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15467433880059136365&hl=en&as_sdt=0,5)]. [[Project](http://visualcommonsense.com/)].
  - Visual Commonsense R-CNN - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6886229776034162585&hl=en&as_sdt=0,5)].
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - From Recognition to Cognition: Visual Commonsense Reasoning - ***CVPR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15467433880059136365&hl=en&as_sdt=0,5)]. [[Project](http://visualcommonsense.com/)].
  - Visual Commonsense R-CNN - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6886229776034162585&hl=en&as_sdt=0,5)].
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - From Recognition to Cognition: Visual Commonsense Reasoning - ***CVPR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15467433880059136365&hl=en&as_sdt=0,5)]. [[Project](http://visualcommonsense.com/)].
  - Visual Commonsense R-CNN - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6886229776034162585&hl=en&as_sdt=0,5)].
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - From Recognition to Cognition: Visual Commonsense Reasoning - ***CVPR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15467433880059136365&hl=en&as_sdt=0,5)]. [[Project](http://visualcommonsense.com/)].
  - Visual Commonsense R-CNN - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6886229776034162585&hl=en&as_sdt=0,5)].
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - From Recognition to Cognition: Visual Commonsense Reasoning - ***CVPR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15467433880059136365&hl=en&as_sdt=0,5)]. [[Project](http://visualcommonsense.com/)].
  - Visual Commonsense R-CNN - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6886229776034162585&hl=en&as_sdt=0,5)].
  - SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks - ***NeurIPS'23***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=3844178012869500706&hl=en&as_sdt=0,5)]. [[Project](https://swiftsage.github.io/)].
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - From Recognition to Cognition: Visual Commonsense Reasoning - ***CVPR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15467433880059136365&hl=en&as_sdt=0,5)]. [[Project](http://visualcommonsense.com/)].
  - Visual Commonsense R-CNN - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6886229776034162585&hl=en&as_sdt=0,5)].
  - wikiHow - ***wikiHow.com***. wikiHow is on website hosting step-by-step "How-to" procedural instructions across various domains and topics.
  - Open Mind Common Sense: Knowledge Acquisition from the General Public - ***OTM Confederated International Conferences'02***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=11431785236825227404&hl=en&as_sdt=0,5)]..
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Visual Concept Programming: A Visual Analytics Approach to Injecting Human Intelligence at Scale - ***IEEE Transactions on Visualization and Computer Graphics***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10724509334112758172&hl=en&as_sdt=0,5)]. This paper presents Visual Concept Programming, a first-of-its-kind visual analytics approach of using visual concepts to program image data at scale while requiring a few human efforts.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations - ***NAACL'24***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=470445696014235795)]. This paper explores the task of uncommonsense abductive reasoning. Given a piece of context with an unexpected outcome, this task requires reasoning abductively to generate an explanation that makes the unexpected outcome more likely in the context.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - The World Avatar - ***The World Avatar™***. A large-scale dynamic knowledge graph connecting concepts with relations to digitalize molecules, buildings, cities, and countries.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
  - Intuitive physics learning in a deep-learning model inspired by developmental psychology - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13803979681049451699&hl=en&as_sdt=0,5)]. A machine-learning dataset designed to evaluate conceptual understanding of intuitive physics, adopting the violation-of-expectation (VoE) paradigm from developmental psychology; a deep-learning system that learns intuitive physics directly from visual data, inspired by studies of visual cognition in children.
  - Phy-Q as a measure for physical reasoning intelligence - ***Nature Machine Intelligence***, 2023. [[NMI Challenge](https://www.nature.com/articles/s42256-019-0072-x)]. An interactive benchmark for AI physical reasoning.
- Inductive Logic & Program Synthesis
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  - First-order Model Theory - ***Plato Stanford***. A computational philosophy account on First-order Model Theory, which is a branch of mathematics that deals with the relationships between descriptions in first-order languages and the structures that satisfy these descriptions.
  - Paraconsistent Logic - ***Plato Stanford***. A computational philosophy account on Paraconsistent Logic, where any logic is paraconsistent as long as it is not explosive.
  - Logical Consequence - ***Plato Stanford***. A computational philosophy account on Logical Consequence, which is about the relation between premises and conclusions in valid arguments.
  - Logic Pluralism - ***Plato Stanford***. A computational philosophy account on Logic Pluralism, which is the view that there is more than one correct logic.
  - The Emergence of First-Order Logic - ***Plato Stanford***. A computational philosophy account on the emergence of first-order logic, mainly about first-order logic is natural retrospect.
  - Second-order and Higher-order Logic - ***Plato Stanford***.
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  - Learning Higher-Order Logic Programs through Abstraction and Invention - ***IJCAI'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=10945054943203858325&hl=en&as_sdt=0,5)].
  - How Much Can Experimental Cost Be Reduced in Active Learning of Agent Strategies? - ***ILP'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=8152380236842970357&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
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  - Inductive logic programming at 30: a new introduction - ***Journal of Artificial Intelligence Research***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=317114056670544302&hl=en&as_sdt=0,5)]. A 30-year comprehensive review on Inductive Logic Programming.
  - Learning programs by learning from failures - ***Machine Learning***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6797200487935462023&hl=en&as_sdt=0,5)].
  - Complete Bottom-Up Predicate Invention in Meta-Interpretive Learning - ***IJCAI'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6085183078630665234&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning as Metarule Specialisation - ***Machine Learning***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=14684315775211086859&hl=en&as_sdt=0,5)].
  - Qualitative choice logic - ***Artificial Intelligence***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=1586187056162326386&hl=en&as_sdt=0,5)].
  - Derivative-free optimization of high-dimensional non-convex functions by sequential random embeddings - ***IJCAI'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=15955040483290586781&hl=en&as_sdt=0,5)].
  - Finitely Generated Groups and First-Order Logic - ***Journal of The London Mathematical Society-second Series***, 2005. [[All Versions](https://scholar.google.com/scholar?cluster=3457158221419711506&hl=en&as_sdt=0,5)].
  - DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning - 2020. [[All Versions](https://scholar.google.com/scholar?cluster=3288385399148303844&hl=en&as_sdt=0,5)]. A incremental learning version of Bayesian program learning.
  - Leveraging Language for Abstraction and Program Search - ***ICML'20***, 2020. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Leveraging+Language+for+Abstraction+and+Program+Search&btnG=)].
  - Program Synthesis Guided Reinforcement Learning - ***NeurIPS'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=17353674428642875269&hl=en&as_sdt=0,5)].
  - Learning Part-Based Abstractions for Visual Object Concepts - ***CogSci'21***, 2021. [[All Versions](https://scholar.google.com/scholar?lookup=0&q=Learning+Part-Based+Abstractions+for+Visual+Object+Concepts&hl=en&as_sdt=0,5)].
  - Program Synthesis with Large Language Models - 2021. [[All Versions](https://scholar.google.com/scholar?cluster=15213050540818392833&hl=en&as_sdt=0,5)].
  - AutumnSynth: Synthesis of Reactive Programs with Structured Latent State - ***NeurIPS'21 AIPLANS Workshop***, 2021. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=AutumnSynth%3A+Synthesis+of+Reactive+Programs+with+Structured+Latent+State&btnG=)].
  - Synthesizing theories of human language with Bayesian program induction - ***Nature Communications***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=8603772394100237159&hl=en&as_sdt=0,5)].
  - From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought - 2023. [[All Versions](https://scholar.google.com/scholar?cluster=13778788929096574993&hl=en&as_sdt=0,5)]. Rational meaning construction, a computational framework for language-informed thinking that combines neural language models with probabilistic models for rational inference. Linguistic meaning is framed as a context-sensitive mapping from natural language into a probabilistic language of thought (PLoT)--a general-purpose symbolic substrate for generative world modeling.
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Latent Programmer: Discrete Latent Codes for Program Synthesis - ***ICML'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=9789877360194738968&hl=en&as_sdt=0,5)]. Paper introducing the Latent Programmer, a program synthesis method that first predicts a discrete latent code from input/output examples, and then generates the program in the target language.
  - PAL: Program-aided Language Models - ***ICML'23***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=14898051625978777315&hl=en&as_sdt=0,5)]. Paper presenting an approach that uses the LLM to read natural language problems and generate programs as the intermediate reasoning steps, but offloads the solution step to a runtime such as a Python interpreter. With PAL, decomposing the natural language problem into runnable steps remains the only learning task for the LLM, while solving is delegated to the interpreter.
  - Large Language Models Meet NL2Code: A Survey - ***ACL'23***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11868015824802341463&hl=en&as_sdt=0,5)]. [[NL2Code Website](https://nl2code.github.io/)]. A paper presenting a comprehensive survey of 27 existing large language models for NL2Code, and also review benchmarks and metrics, suggesting that the key factors contributing to the success of large language models for NL2Code are “Large Size, Premium Data, Expert Tuning”.
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - - 2023. [[All Versions](https://scholar.google.com/scholar?cluster=10466731638053452642&as_sdt=0,5)]. A systematic literature review on LLM4SE, with a particular focus on understanding how LLMs can be exploited to optimize processes and outcomes.
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges - ***ICSE'24***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3696356619002071917&hl=en&as_sdt=0,5)]. A survey finding that developers are most motivated to use AI programming assistants because they help developers reduce key-strokes, finish programming tasks quickly, and recall syntax, but resonate less with using them to help brainstorm potential solutions.
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
  - Meta-Interpretive Learning from noisy images - ***Machine Learning***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=5719375383968868329&hl=en&as_sdt=0,5)].
- Knowledge Representation
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Handbook of Knowledge Representation - ***Elsevier***, 2008. [[All Versions](https://scholar.google.com/scholar?cluster=14732064619564679879&hl=en&as_sdt=0,5)]. A pragmatical handbook for all kinds of knowledge representation modes.
  - Logic and Ontology - ***Plato Stanford***. A computational philosophy account on logic and ontology, mainly about the intersections of logic and ontology in many significant philosophy problems.
  - The Language of Thought Hypothesis - ***Plato Stanford***. A computational philosophy account on the laugnage of though hypothesis, which proposes that thinking occurs in a mental language.
  - The Analysis of Knowledge - ***Plato Stanford***.
  - Scientific Representation - ***Plato Stanford***. A computational philosophy account on scientific representation, focusing on how scientific models represent their target systems.
  - Common Knowledge - ***Plato Stanford***.
  - Sense-Data - ***Plato Stanford***.
  - Supervenience - ***Plato Stanford***. A computational philosophy account on supervenience, where a set of properties A supervenes upon another set B just in case no two things can differ with respect to A-properties without also differing with respect to their B-properties.
  - Dialogical Logic - ***Plato Stanford***. A computational philosophy account on dialogical logic, which is a dialogue-based approach to logic and argumentation rooted in a research tradition that goes back to dialectics in Greek Antiquity, when problems were approached through dialogues in which opposing parties discussed a thesis through questions and answers.
  - Temporal Logic - ***Plato Stanford***.
  - Modal Logic - ***Plato Stanford***. A computational philosophy account on Modal Logic, which is the study of the deductive behavior of the expressions 'it is necessary that' and 'it is possible that'.
  - Epistemic Logic - ***Plato Stanford***. A computational philosophy account on Epistemic Logic, which is a subfield of epistemology concerned with logical approaches to knowledge, belief and related notions.
  - Epistemic Modal Logic - ***Wikipedia***.
  - The Perception of Relations - ***Trends in Cognitive Sciences***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12190078466818849725&hl=en&as_sdt=2005&sciodt=0,5)]. Chaz Firestone's review on the perception of relation, in constrast to the conventional reasoning view.
  - Commonsense reasoning about causality: Deriving behavior from structure - ***Artificial Intelligence***, 1984. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=14940738362673077704)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - A Translation Approach to Portable Ontology Specifications - ***Knowledge Acquisition***, 1993. [[All Versions](https://scholar.google.com/scholar?cluster=14668658395073605123&hl=en&as_sdt=0,5)].
  - Answer Set Programming - ***ICLPNR'99***, 1999. [[All Versions](https://scholar.google.com/scholar?cluster=15267370435063454675&hl=en&as_sdt=0,5)]. The original paper on Answer Set Programming (ASP).
  - The Symbolic Grounding Problem - ***Physica D: Nonlinear Phenomena***, 1990. [[All Versions](https://scholar.google.com/scholar?cluster=6279614024681929496&hl=en&as_sdt=0,5)].
  - Learning overhypotheses with hierarchical Bayesian models - ***Developmental Science***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=18041836774924845900&hl=en&as_sdt=0,5)].
  - Learning Causal Schemata - ***CogSci'07***, 2007, [[All Versions](https://scholar.google.com/scholar?cluster=5008191267417189643&hl=en&as_sdt=0,5)].
  - The discovery of structural form - ***Proceedings of the National Academy of Sciences***, 2008. [[All Versions](https://scholar.google.com/scholar?cluster=10433149156915110486&hl=en&as_sdt=0,5)]. Chales Kemp's review on theory induction.
  - A Rational Analysis of Rule-Based Concept Learning - ***Cognitive Science***, 2008. [[All Versions](https://scholar.google.com/scholar?cluster=7765061503727822620&hl=en&as_sdt=0,5)].
  - Modeling semantic cognition as logical dimensionality reduction - ***CogSci'08***, 2008. [[All Versions](https://scholar.google.com/scholar?cluster=17061801746839695691&hl=en&as_sdt=0,5)].
  - Theory Acquisition and the Language of Thought - ***CogSci'08***, 2008. [[All Versions](https://scholar.google.com/scholar?cluster=1839916602381147749&hl=en&as_sdt=0,5)].
  - Theory Acquisition as Stochastic Search - ***CogSci'10***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=16324634056226561429&hl=en&as_sdt=0,5)].
  - A probabilistic model of theory formation - ***Cognition***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=7705799129887482041&hl=en&as_sdt=0,5)].
  - Bootstrapping in a language of thought: A formal model of numerical concept learning - ***Cognition***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=13046606910781656302&hl=en&as_sdt=0,5)].
  - Concepts in a Probabilistic Language of Thought - ***Center for Brains, Minds, and Machines MEMO No.010***, 2014. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=14593712389828476130)].
  - Exploring the Conceptual Universe - ***Psychological Review***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=17824067813343816306&hl=en&as_sdt=0,5)].
  - A taxonomy of inductive problems - ***Psychonomic Bulletin & Review***, 2014. [[All Versions](https://scholar.google.com/scholar?cluster=2571009743105592927&hl=en&as_sdt=0,5)].
  - The Logical Primitives of Thought: Empirical Foundations for Compositional Cognitive Models - ***Psychological Review***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=5316027496661813145&hl=en&as_sdt=0,5)].
  - The Emergence of Organizing Structure in Conceptual Representation - ***Cognitive Science***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=4986316323923233074&hl=en&as_sdt=0,5)].
  - Theory Acquisition as Constraint-Based Program Synthesis - ***CogSci'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=525148607069840280&hl=en&as_sdt=0,5)].
  - Connecting perceptual and procedural abstractions in physical construction - ***CogSci'21***, 2021. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Connecting+perceptual+and+procedural+abstractions+in+physical+construction&btnG=)].
  - Invariant representation of physical stability in the human brain - 2021. [[All Versions](https://scholar.google.com/scholar?cluster=17431019238600295521&hl=en&as_sdt=0,5)].
  - Introduction to The Fluent Calculus - ***Linkoeping University Electronic Press***, 1998. [[All Versions](https://scholar.google.com/scholar?cluster=12069059079023496731&hl=en&as_sdt=0,5)].
  - From situation calculus to fluent calculus: State update axioms as a solution to the inferential frame problem - ***Artificial Intelligence***, 1999. [[All Versions](https://scholar.google.com/scholar?cluster=10854895617698839149&hl=en&as_sdt=0,5)].
  - Unsupervised Structure Learning of Stochastic And-Or Grammars - ***NeurIPS'13***, 2013. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=4354984630817844670)].
  - Algorithms of Adaptation in Inductive Inference - 2021. [[All Versions](https://scholar.google.com/scholar?cluster=16222039361294164246&hl=en&as_sdt=0,5)].
  - A representational analysis of numeration systems - ***Cognition***, 1995. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=8852566070856662412)].
  - Learning Program Representations for Food Images and Cooking Recipes - ***CVPR'22***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=7690010749576063125)].
  - Biocoder: A programming language for standardizing and automating biology protocols - ***Journal of Biological Engineering***, 2010. [[All Versions](https://scholar.google.com/scholar?start=0&hl=en&as_sdt=0,5&cluster=15572197190838916795)]. Microsoft's programming language for representing biology protocols.
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Self-Knowledge - ***Plato Stanford***. A computational philosophy account on self-knowledge, which standardly refers to knowledge of one's own mental states—that is, of what one is feeling or thinking, or what one believes or desires.
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Reasoning about Procedures with Natural Language Processing: A Tutorial - 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11364086808527515615&hl=en&as_sdt=0,5)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
  - Logics for Epistemic Programs - ***Synthese***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=11403619699670839488&hl=en&as_sdt=0,5&as_vis=1)].
- Cognitive Development
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Machine Common Sense Concept Paper - ***DARPA***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=1603121108181262769&hl=en&as_sdt=0,5)]. DARPA's perspective on integrating core knowledge from development psychology into machine intelligence systems.
  - Cognitive Development - ***Wikipedia***.
  - Cognitive development: An information processing approach - ***B.Blackwell***, 1991. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Cognitive+development%3A+An+information+processing+approach&btnG=)].
  - Towards a rational constructivist theory of cognitive development - ***Psychological Review***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=3294824172745724080&hl=en&as_sdt=0,5)]. Fei Xu's review extending Gopnik's view of constructivism, with the rationality as constraint.
  - The origins of inquiry: inductive inference and exploration in early childhood - ***Trends in Cognitive Sciences***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=5189329081728071335&hl=en&as_sdt=0,5)]. Laura Schulz's review on children's exploratory play.
  - Play, Curiosity, and Cognition - ***Annual Review of Developmental Psychology***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=10278208468154249192&hl=en&as_sdt=2005&sciodt=0,5)]. Laura Schulz's review on children's exploratory play, which proposes a new perspective on exploratory play to explain the emergence of irrational behaviors in play.
  - From exploration to play: A cross-sectional study of infant free play behavior - ***Developmental Psychology***, 1981. [[All Versions](https://scholar.google.com/scholar?cluster=15547331535034599545&hl=en&as_sdt=0,5)].
  - Detecting Blickets: How Young Children Use Information about Novel Causal Powers in Categorization and Induction - ***Children Development***, 2003. [[All Versions](https://scholar.google.com/scholar?cluster=9049737233568227380&hl=en&as_sdt=0,5)].
  - Serious fun: Preschoolers engage in more exploratory play when evidence is confounded - ***Developmental Psychology***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=3033619407322882147&hl=en&as_sdt=0,5)].
  - Observing the unexpected enhances infants' learning and exploration - ***Science***, 2015. [[All Versions](https://scholar.google.com/scholar?start=10&hl=en&as_sdt=0,5&cluster=9247917261616759689)].
  - Word, thought, and deed: the role of object categories in children's inductive inferences and exploratory play - ***Developmental Psychology***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=13947689064550390312&hl=en&as_sdt=0,5)].
  - Where science starts: Spontaneous experiments in preschoolers' exploratory play - ***Cognition***, 2011. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=16321989770180281706)].
  - Scientific thinking in young children: Theoretical advances, empirical research, and policy implications - ***Science***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=9103846738385460508&hl=en&as_sdt=2005)].
  - Finding New Facts; Thinking New Thoughts - ***Advances in Child Development and Behavior***, 2012. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Finding+new+facts%3B+thinking+new+thoughts&btnG=)].
  - Theory learning as stochastic search in the language of thought - ***Cognitive Development***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=8036476579458645432&hl=en&as_sdt=0,5)].
  - Infants make more attempts to achieve a goal when they see adults persist - ***Science***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=2617011825272996810&hl=en&as_sdt=0,5)].
  - Knowing when to quit: Children consider access to solutions when deciding whether to persist - ***CogSci'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=15997297570269958414&hl=en&as_sdt=2005&sciodt=0,5)].
  - Sticking to the Evidence? A Behavioral and Computational Case Study of Micro-Theory Change in the Domain of Magnetism - ***Cognitive Science***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4409900195679222965&hl=en&as_sdt=2005&sciodt=0,5)].
  - Cognitive pragmatism: Children flexibly choose between facts and conjectures - ***CogSci'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=6978944437676543728&hl=en&as_sdt=2005&sciodt=0,5)].
  - Children selectively endorse speculative conjectures - ***Child Development***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=5672344544260882286&hl=en&as_sdt=2005&sciodt=0,5)].
  - Learning higher-order generalizations through free play: Evidence from 2- and 3-year-old children - ***Developmental Psychology***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=4386474921214936914&hl=en&as_sdt=0,5)].
  - The Child as Hacker - ***Trends in Cognitive Sciences***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=13128656954836679743&hl=en&as_sdt=2005&sciodt=0,5&as_ylo=2017)].
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - A Developmental Perspective on Executive Function - ***Child Development***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11347590808138984649&hl=en&as_sdt=0,5)].
  - Rethinking Executive Function and Its Development - ***Psychological Science***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=16570230278367237499&hl=en&as_sdt=2005&sciodt=0,5)].
  - Perception of partly occluded objects in infancy - ***Cognitive Psychology***, 1983. [[All Versions](https://scholar.google.com/scholar?cluster=4182861116190610992&hl=en&as_sdt=0,5)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Bayesian Models of Conceptual Development: Learning as Building Models of the World - ***Annual Review of Developmental Psychology***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=646614032563248495&hl=en&as_sdt=2005&sciodt=0,5)].
  - Exploratory play, rational action, and efficient search - ***CogSci'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17529638197045429028&hl=en&as_sdt=0,5)].
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Childhood as a solution to explore–exploit tensions - ***Philosophical Transactions of the Royal Society B: Biological Sciences***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=11960188575664977017&hl=en&as_sdt=2005&sciodt=0,5)].
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Childhood as a solution to explore–exploit tensions - ***Philosophical Transactions of the Royal Society B: Biological Sciences***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=11960188575664977017&hl=en&as_sdt=2005&sciodt=0,5)].
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Play, Curiosity, and Cognition - ***Annual Review of Developmental Psychology***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=10278208468154249192&hl=en&as_sdt=2005&sciodt=0,5)]. Laura Schulz's review on children's exploratory play, which proposes a new perspective on exploratory play to explain the emergence of irrational behaviors in play.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Children's exploratory play tracks the discriminability of hypotheses - ***Nature Communications***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=12389351553206792907&hl=en&as_sdt=0,5&as_ylo=2020)].
  - Age-of-acquisition ratings for 30,000 English words - ***Behavior Research Methods***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=6752414178722956940&hl=en&as_sdt=0,5)]. [[Project](http://crr.ugent.be/archives/806)]. A database for age-of-acquisition ratings for over 30k English words.
  - Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory - ***Psychological Bulletin***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=11218217347365817167&hl=en&as_sdt=0,5)]. Alison Gopnik's review on the constructivism idea of developmental research.
- Learning in the Open World
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  - Online learning of symbolic concepts - ***Journal of Mathematical Psychology***, 2017. [[All Versions](https://scholar.google.com/scholar?start=20&hl=en&as_sdt=2005&sciodt=0,5&cites=8036476579458645432&scipsc=)].
  - Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly - ***IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=11909080239486864961&hl=en&as_sdt=0,5)]. A comprehensive review on zero-shot learning.
  - Generalizing from a few examples: A survey on few-shot learning - ***ACM Computing Survey***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7932202448069313464&hl=en&as_sdt=0,5)].
  - Towards Open World Recognition - ***CVPR'15***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=856704237994181529&hl=en&as_sdt=0,5)]. The first paper introducing the problem of open-world recognition.
  - Towards Open Set Deep Networks - ***CVPR'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=3571743951915089896&hl=en&as_sdt=0,5)].
  - In the Wild: From ML Models to Pragmatic ML Systems - ***ICLR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=15243890330014986346&hl=en&as_sdt=0,5)]. A comprehensive review on incremental machine learning.
  - Adversarial Filters of Dataset Biases - ***ICML'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=11617966867048191189&hl=en&as_sdt=0,5)].
  - A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning - 2020. [[All Versions](https://scholar.google.com/scholar?cluster=2640432662088551010&hl=en&as_sdt=0,5)].
  - Energy-Based Models for Continual Learning - ***NeurIPS'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7094884707139778576&hl=en&as_sdt=0,5)]. [[Project](https://energy-based-model.github.io/Energy-Based-Models-for-Continual-Learning/)].
  - Learning to Learn Image Classifiers with Visual Analogy - ***CVPR'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=6285495755337309034&hl=en&as_sdt=0,5)].
  - Zero-Shot Object Detection - ***ECCV'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=2027060030559987993&hl=en&as_sdt=0,5)].
  - Towards Open World Object Detection - ***CVPR'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=9715328489246217151&hl=en&as_sdt=0,5)]. [[Project](https://github.com/JosephKJ/OWOD)].
  - Learning to Recognise Unseen Classes by A Few Similes - ***MM'17***, 2017. [[All Versions](https://scholar.google.com/scholar?q=related:FZZr2BK0U6YJ:scholar.google.com/&scioq=Learning+to+Recognise+Unseen+Classes+by+A+Few+Similes&hl=en&as_sdt=0,5)].
  - Ontology-guided Semantic Composition for Zero-Shot Learning - ***KR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1825132732653262003&hl=en&as_sdt=0,5)].
  - OntoZSL: Ontology-enhanced Zero-shot Learning - ***WWW'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1042573079110416209&hl=en&as_sdt=0,5)].
  - Knowledge-aware Zero-Shot Learning: Survey and Perspective - ***IJCAI'21*** 2021. [[All Versions](https://scholar.google.com/scholar?cluster=2596179801089642923&hl=en&as_sdt=0,5)].
  - From Red Wine to Red Tomato: Composition with Context - ***CVPR'17***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=6959320578989247472&hl=en&as_sdt=0,5)].
  - Attributes as Operators: Factorizing Unseen Attribute-Object Compositions - ***ECCV'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=11627198158637727139&hl=en&as_sdt=0,5)].
  - Learning Compositional Representations for Few-Shot Recognition - ***CVPR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7363445845219257348&hl=en&as_sdt=0,5)].
  - Symmetry and Group in Attribute-Object Compositions - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=16870815556752021056&hl=en&as_sdt=0,5)].
  - A causal view of compositional zero-shot recognition - ***NeurIPS'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=2543173389101020482&hl=en&as_sdt=0,5)].
  - Compositional Few-Shot Recognition with Primitive Discovery and Enhancing - ***MM'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=15817839338790433509&hl=en&as_sdt=0,5)].
  - Learning Unseen Concepts via Hierarchical Decomposition and Composition - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=14161656227038242300&hl=en&as_sdt=0,5)].
  - Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly - ***IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=11909080239486864961&hl=en&as_sdt=0,5)]. A comprehensive review on zero-shot learning.
  - Towards Open Set Deep Networks - ***CVPR'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=3571743951915089896&hl=en&as_sdt=0,5)].
  - Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly - ***IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=11909080239486864961&hl=en&as_sdt=0,5)]. A comprehensive review on zero-shot learning.
  - Towards Open Set Deep Networks - ***CVPR'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=3571743951915089896&hl=en&as_sdt=0,5)].
  - Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly - ***IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=11909080239486864961&hl=en&as_sdt=0,5)]. A comprehensive review on zero-shot learning.
  - Towards Open Set Deep Networks - ***CVPR'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=3571743951915089896&hl=en&as_sdt=0,5)].
  - Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly - ***IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=11909080239486864961&hl=en&as_sdt=0,5)]. A comprehensive review on zero-shot learning.
  - Towards Open Set Deep Networks - ***CVPR'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=3571743951915089896&hl=en&as_sdt=0,5)].
  - Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly - ***IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=11909080239486864961&hl=en&as_sdt=0,5)]. A comprehensive review on zero-shot learning.
  - Towards Open Set Deep Networks - ***CVPR'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=3571743951915089896&hl=en&as_sdt=0,5)].
  - Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly - ***IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=11909080239486864961&hl=en&as_sdt=0,5)]. A comprehensive review on zero-shot learning.
  - Towards Open Set Deep Networks - ***CVPR'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=3571743951915089896&hl=en&as_sdt=0,5)].
  - Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly - ***IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=11909080239486864961&hl=en&as_sdt=0,5)]. A comprehensive review on zero-shot learning.
  - Towards Open Set Deep Networks - ***CVPR'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=3571743951915089896&hl=en&as_sdt=0,5)].
  - Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly - ***IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=11909080239486864961&hl=en&as_sdt=0,5)]. A comprehensive review on zero-shot learning.
  - Towards Open Set Deep Networks - ***CVPR'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=3571743951915089896&hl=en&as_sdt=0,5)].
  - Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly - ***IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=11909080239486864961&hl=en&as_sdt=0,5)]. A comprehensive review on zero-shot learning.
  - Towards Open Set Deep Networks - ***CVPR'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=3571743951915089896&hl=en&as_sdt=0,5)].
  - Towards Open Set Deep Networks - ***CVPR'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=3571743951915089896&hl=en&as_sdt=0,5)].
  - Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly - ***IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=11909080239486864961&hl=en&as_sdt=0,5)]. A comprehensive review on zero-shot learning.
- Learning with Cognitive Plausibility
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  - Motion Reasoning for Goal-Based Imitation Learning - ***ICRA'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7519230802512388210&hl=en&as_sdt=0,5)].
  - Describing Objects by their Attributes - ***CVPR'09***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=6853730684095116174&hl=en&as_sdt=0,5)].
  - Accuracy and Precision - ***Wikipedia***. Wikipedia on the distinctions and the trade-off between accuracy and precision.
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  - Recognition-by-Components: A Theory of Human Image Understanding - ***Psychological Review***, 1987. [[All Versions](https://scholar.google.com/scholar?cluster=16522931798979362446&hl=en&as_sdt=0,5)]. The original paper on the recognition-by-components theory.
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense - ***Engineering***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=12292747257300299161&hl=en&as_sdt=0,5)]. Yixin Zhu and Song-Chun Zhu's review on visual commonsense.
  - Self-supervised Learning Through the eyes of a Child - ***NeurIPS'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=5608715260418451299&hl=en&as_sdt=0,5)]. Concept learning through near-natural co-occurrence frequency estimation.
  - CLEVRER: CoLlision Events for Video REpresentation and Reasoning - ***ICLR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4352064462350202338&hl=en&as_sdt=0,5)].
  - BONGARD-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning - ***NeurIPS'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=9164011458889391917&hl=en&as_sdt=0,5)].
  - The relationship between Precision-Recall and ROC curves - ***ICML'06***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=10708180947310062390&hl=en&as_sdt=0,5)].
  - Distributional Generalization: A New Kind of Generalization - 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6190621467796247477&hl=en&as_sdt=0,5)].
  - Learning and development in networks: The importance of starting small. - ***Cognition***, 1993. [[All Versions](https://scholar.google.com/scholar?cluster=5133345254007462915&hl=en&as_sdt=0,5)]. The original paper on the idea of curriculum learning.
  - Language acquisition in the absence of explicit negative evidence: how important is starting small? - ***Cognition***, 1999. [[All Versions](https://scholar.google.com/scholar?cluster=11813578367725362166&hl=en&as_sdt=0,5)].
  - Curriculum Learning - ***ICML'09***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=8740915934335425405&hl=en&as_sdt=0,5)]. The original paper applying the idea of curriculum learning to machine learning.
  - Parsing video events with goal inference and intent prediction - ***ICCV'11***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=5979196784405021658&hl=en&as_sdt=0,5)].
  - Inferring "Dark Matter" and "Dark Energy" from Videos - ***ICCV'13***, 2013. [[All Versions](https://scholar.google.com/scholar?cluster=3467068307444498624&hl=en&as_sdt=0,5)]. The original paper on latent state discovery from videos.
  - Explainable and Explicit Visual Reasoning over Scene Graphs - ***CVPR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=8517395712319798436&hl=en&as_sdt=0,5)].
  - Attention over Learned Object Embeddings Enables Complex Visual Reasoning - ***NeurIPS'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=127829313460149801&hl=en&as_sdt=0,5)].
  - Distributed Representations of Words and Phrases and their Compositionality - ***NeurIPS'13***, 2013. [[All Versions](https://scholar.google.com/scholar?cluster=2410615501856807729&hl=en&as_sdt=0,5)].
  - Motion Reasoning for Goal-Based Imitation Learning - ***ICRA'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7519230802512388210&hl=en&as_sdt=0,5)].
  - Action Genome: Actions as Compositions of Spatio-temporal Scene Graphs - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=388714326304810525&hl=en&as_sdt=0,5)].
  - Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals - ***NeurIPS'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=2255457416066730255&hl=en&as_sdt=0,5)].
  - Something-Else: Compositional Action Recognition with Spatial-Temporal Interaction Networks - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=17469863154797360929&hl=en&as_sdt=0,5)].
  - Putting visual object recognition in context - ***CVPR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=6207193649298787857&hl=en&as_sdt=0,5)].
  - Multimodal Few-Shot Learning with Frozen Language Models - 2021. [[All Versions](https://scholar.google.com/scholar?cluster=16154696122208258147&hl=en&as_sdt=0,5)].
  - Describing Objects by their Attributes - ***CVPR'09***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=6853730684095116174&hl=en&as_sdt=0,5)].
  - Panoramic Learning with A Standardized Machine Learning Formalism - 2021. [[All Versions](https://scholar.google.com/scholar?cluster=14222434793711614257&hl=en&as_sdt=0,5)].
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  - Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms - ***ICLR'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=2486025806014234529&hl=en&as_sdt=0,5)].
  - Interplay between rule learning and rule switching in a perceptual categorization task - 2022. [[All Versions](https://scholar.google.com/scholar?cluster=7461559646167397406&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Describing Objects by their Attributes - ***CVPR'09***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=6853730684095116174&hl=en&as_sdt=0,5)].
  - Motion Reasoning for Goal-Based Imitation Learning - ***ICRA'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7519230802512388210&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Motion Reasoning for Goal-Based Imitation Learning - ***ICRA'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7519230802512388210&hl=en&as_sdt=0,5)].
  - Describing Objects by their Attributes - ***CVPR'09***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=6853730684095116174&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Motion Reasoning for Goal-Based Imitation Learning - ***ICRA'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7519230802512388210&hl=en&as_sdt=0,5)].
  - Describing Objects by their Attributes - ***CVPR'09***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=6853730684095116174&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Motion Reasoning for Goal-Based Imitation Learning - ***ICRA'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7519230802512388210&hl=en&as_sdt=0,5)].
  - Describing Objects by their Attributes - ***CVPR'09***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=6853730684095116174&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
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  - Describing Objects by their Attributes - ***CVPR'09***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=6853730684095116174&hl=en&as_sdt=0,5)].
  - Cognitive Science: Definition, Status, and Questions - ***Annual Review of Psychology***, 1989. [[All Versions](https://scholar.google.com/scholar?cluster=8549671583307260475&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
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  - Describing Objects by their Attributes - ***CVPR'09***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=6853730684095116174&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
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  - Describing Objects by their Attributes - ***CVPR'09***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=6853730684095116174&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Motion Reasoning for Goal-Based Imitation Learning - ***ICRA'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=7519230802512388210&hl=en&as_sdt=0,5)].
  - Describing Objects by their Attributes - ***CVPR'09***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=6853730684095116174&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
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  - Describing Objects by their Attributes - ***CVPR'09***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=6853730684095116174&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
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  - Describing Objects by their Attributes - ***CVPR'09***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=6853730684095116174&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
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  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
  - Machine Behaviour - ***Nature***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=7881171273277686092&hl=en&as_sdt=0,5)].
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- Tasks & Environments
  - The Evolution of First Person Vision Methods: A Survey - ***Trans. CSVT***, 2015.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video - ***Trans. PAMI***, 2020.
  - A Dataset and Architecture for Visual Reasoning with a Working Memory - ***ECCV'18***, 2018. [[Project](https://github.com/google/cog)].
  - PHYRE: A New Benchmark for Physical Reasoning - ***NeurIPS'19***, 2019.
  - CATER: A diagnostic dataset for Compositional Actions & TEmporal Reasoning - ***ICLR'20***, 2020. [[Project](https://rohitgirdhar.github.io/CATER/)].
  - CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning
  - ThreeDWorld - ***MIT-IBM***. [[Paper](https://arxiv.org/abs/2007.04954)].
  - Rearrangement: A Challenge for Embodied AI
  - iGibson - ***Stanford***. [[Paper](https://ieeexplore.ieee.org/document/8954627)].
  - AI2-THOR - ***Allen Institute***. [[Paper](https://arxiv.org/abs/1712.05474)].
  - Robo-THOR - ***Allen Institute***. [[Paper](https://arxiv.org/abs/2004.06799)].
  - Manipula-THOR - ***Allen Institute***. [[Paper](https://arxiv.org/abs/2104.11213)].
  - RLBench - ***Imperial College***. [[Paper](https://ieeexplore.ieee.org/document/9001253)].
  - First-Person Vision - ***Proceedings of the IEEE***, 2012.
  - The Evolution of First Person Vision Methods: A Survey - ***Trans. CSVT***, 2015.
  - Understanding the Nature of First-Person Videos: Characterization and Classification using Low-Level Features - ***CVPR'14***, 2014.
  - Pooled Motion Features for First-Person Videos - ***CVPR'15***, 2015.
  - Actor and Observer: Joint Modeling of First and Third-Person Videos - ***CVPR'18***, 2018.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video - ***Trans. PAMI***, 2020.
  - View-Action Representation Learning for Active First-Person Vision - ***Trans. CSVT***, 2021.
  - Design and Use Paradigms for Gazebo, An Open-Source Multi-Robot Simulator - ***IROS'04***, 2004. [[Project](http://gazebosim.org/)].
  - ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning - ***CIG'16***, 2016. [[Project](http://vizdoom.cs.put.edu.pl/)].
  - Is First Person Vision Challenging for Object Tracking? The TREK-100 Benchmark Dataset
  - [Project
  - On the Measure of Intelligence - ***Google Research***, 2019.
  - AI-Birds - ***IJCAI***.
  - Hi-Phy: A Benchmark for Hierarchical Physical Reasoning
  - Mining Learning and Crafting Scientific Experiments: A Literature Review on the Use of Minecraft in Education and Research - ***Journal on Eduction Technology & Society***, 2016.
  - The Malmo Platform for Artificial Intelligence Experimentation
  - [Malmo-env
  - [Malmo-Tutorials
  - [MineRL
  - Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft
  - Learning to execute instructions in a Minecraft dialogue - ***ACL'20***, 2020.
  - Collaborative Dialogue in Minecraft - ***ACL'19***, 2019.
  - Learning Skill Hierarchies from Predicate Descriptions and Self-Supervision - ***AAAI GenPlan Workshop***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - AMRL: Aggregated Memory for Reinforcement Learning - ***ICLR'20***, 2020.
  - MineRL: A Large-Scale Dataset of Minecraft Demonstrations
  - Design Mining for Minecraft Architecture - ***AAAI'18***, 2018.
  - Adaptive Agents in Minecraft: A Hybrid Paradigm for Combining Domain Knowledge with Reinforcement Learning - ***AAMAS'17***, 2017.
  - Asynchronous Data Aggregation for Training End to End Visual Control Networks - ***AAMAS'17***, 2017.
  - A Deep Hierarchical Approach to Lifelong Learning in Minecraft - ***AAAI'17***, 2017.
  - Modular Multitask Reinforcement Learning with Policy Sketches - ***ICML'17***, 2017.
  - Control of memory, active perception, and action in minecraft - ***ICML'16***, 2016.
  - How Players Speak to an Intelligent GameCharacter Using Natural Language Messages - ***DiGRA***, 2018.
  - Minecraft as a Generative Platform for Analyzing and Practicing Spatial Reasoning - ***Spatial Cognition'20***, 2020.
  - Generative Design in Minecraft: Chronicle Challenge - ***ICCC'20***, 2020.
  - Minecraft as a Platform for Project-Based Learning in AI - ***AAAI'20***, 2020.
  - MC-Saar-Instruct: a Platform for Minecraft Instruction Giving Agents - ***SIGDial'20***, 2020.
  - The Evolution of First Person Vision Methods: A Survey - ***Trans. CSVT***, 2015.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video - ***Trans. PAMI***, 2020.
  - The Evolution of First Person Vision Methods: A Survey - ***Trans. CSVT***, 2015.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video - ***Trans. PAMI***, 2020.
  - The Evolution of First Person Vision Methods: A Survey - ***Trans. CSVT***, 2015.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video - ***Trans. PAMI***, 2020.
  - The Evolution of First Person Vision Methods: A Survey - ***Trans. CSVT***, 2015.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video - ***Trans. PAMI***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - The Evolution of First Person Vision Methods: A Survey - ***Trans. CSVT***, 2015.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video - ***Trans. PAMI***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - The Evolution of First Person Vision Methods: A Survey - ***Trans. CSVT***, 2015.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video - ***Trans. PAMI***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - The Evolution of First Person Vision Methods: A Survey - ***Trans. CSVT***, 2015.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video - ***Trans. PAMI***, 2020.
  - The Evolution of First Person Vision Methods: A Survey - ***Trans. CSVT***, 2015.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video - ***Trans. PAMI***, 2020.
  - The Evolution of First Person Vision Methods: A Survey - ***Trans. CSVT***, 2015.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video - ***Trans. PAMI***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - The Evolution of First Person Vision Methods: A Survey - ***Trans. CSVT***, 2015.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video - ***Trans. PAMI***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
  - Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video - ***ECCV'20***, 2020.
- Bayesian Modeling
  - Bayesian Epistemology - ***Plato Stanford***. A computational philosophy account on the nature of uncertainty modeling in Bayesian Epistemology.
  - Probabilistic machine learning and artificial intelligence - ***Nature***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=1783282361269717744&hl=en&as_sdt=0,5)]. Zoubin Ghahramani's review on Bayesian machine learning.
  - Generalization, similarity, and Bayesian inference - ***Behavioral and Brain Sciences***, 2001. [[All Versions](https://scholar.google.com/scholar?cluster=14074987155133342565&hl=en&as_sdt=0,5)]. Josh Tenenbaum's review on Bayesian generalization.
  - Bayesian modeling of human concept learning - ***NeurIPS'98***, 1998. [[All Versions](https://scholar.google.com/scholar?cluster=12185543141957001794&hl=en&as_sdt=0,5&as_vis=1)]. Original paper on Bayesian generalization.
  - Rules and Similarity in Concept Learning - ***NeurIPS'99***, 1999. [[All Versions](https://scholar.google.com/scholar?cluster=10968021160883668417&hl=en&as_sdt=0,5)]. Unifying rule-based and similarity-based generalization via Bayesian generalization.
  - Theory-based Bayesian models of inductive learning and reasoning - ***Trends in Cognitive Sciences***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=6741344960992898446&hl=en&as_sdt=0,5)]. Josh Tenenbaum's review on Bayesian theory induction.
  - Word learning as Bayesian inference - ***Psychological Review***, 2007. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=5476233692839102256)]. [[APA](https://psycnet.apa.org/doiLanding?doi=10.1037%2F0033-295X.114.2.245)]. Fei Xu's review on Bayesian word learning.
  - How to Grow a Mind: Statistics, Structure, and Abstraction - ***Science***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=2667398573353002097&hl=en&as_sdt=0,5)]. Josh Tenenbaum's review on Bayesian theory induction.
  - Human-level concept learning through probabilistic program induction. - ***Science***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=11844685101409624506&hl=en&as_sdt=0,5)]. [[Supplementary Material](https://cims.nyu.edu/~brenden/LakeEtAl2015Science_supp.pdf)]. Bayesian program induction for few-shot learning.
  - Building Machines That Learn and Think Like People - ***Behavioral and Brain Sciences***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=8504723689348856287&hl=en&as_sdt=0,5)]. Brenden Lake and Josh Tenenbaum's review on Bayesian modeling.
  - The rational basis of representativeness - ***CogSci'01***, 2001. [[All Versions](https://scholar.google.com/scholar?cluster=11464039134248091466&hl=en&as_sdt=0,5)].
  - Testing a Bayesian Measure of Representativeness Using a Large Image Database - ***NeurIPS'11***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=8576570792794301292&hl=en&as_sdt=0,5)].
  - Constructing a hypothesis space from the Web for large-scale Bayesian word learning - ***CogSci'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=9266416266046851766&hl=en&as_sdt=0,5)].
  - Modeling rules and similarity in colexification - ***CogSci'21***, 2021. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=11578380234126546169)]. Rule- and similarity-based generalization in colexification.
  - Bayesian Data Analysis - ***Chapman and Hall/CRC***, 1995. [[All Versions](https://scholar.google.com/scholar?cluster=5067275302121330689&hl=en&as_sdt=0,5)]. Don Rubin's introductory book on Bayesian models.
  - Filters, random fields and maximum entropy (FRAME): Towards a unified theory for texture modeling - ***International Journal of Computer Vision***, 1998. [[All Versions](https://scholar.google.com/scholar?cluster=11604954524863138240&hl=en&as_sdt=0,5)]. Song-Chun Zhu's original paper on energy-based generative texture modeling.
  - Object Perception as Bayesian Inference - ***Annual Review of Psychology***, 2004. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=1611451804975333652)]. Alan Yuille's review on Bayesian object perception.
  - A tale of three probabilistic families: Discriminative, descriptive, and generative models - ***Quarterly of Applied Mathematics***, 2018. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=6129609629126793774)]. Ying Nian Wu's review on three families of statistical modeling.
  - From information scaling of natural images to regimes of statistical models - ***Quarterly of Applied Mathematics***, 2008. [[All Versions](https://scholar.google.com/scholar?start=0&hl=en&as_sdt=0,5&cluster=17387130978932998303)]. A statistical account for the shift from textons to texture.
  - A Theory of Generative ConvNet - ***ICML'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=11062907630625111054&hl=en&as_sdt=2005&sciodt=0,5)].
  - Cooperative Training of Descriptor and Generator Networks - ***IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=18202808849093155435&hl=en&as_sdt=0,5)].
  - Learning Latent Space Energy-Based Prior Model - ***NeurIPS'20***, 2020. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=9945264852135249894)]. [[Project](https://bpucla.github.io/latent-space-ebm-prior-project/)]. [[Code](https://github.com/bpucla/latent-space-EBM-prior)]. A milestone paper on Latent Energy-Based Model.
  - Learning Energy-Based Models by Diffusion Recovery Likelihood - ***ICLR'21***, 2021. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=4399294843209736764)]. [[Code](https://github.com/ruiqigao/recovery_likelihood)].
  - Score-Based Generative Modeling through Stochastic Differential Equations - ***ICLR'21***, 2021. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=14592788616550656262)].
  - Latent Space Factorisation and Manipulation via Matrix Subspace Projection - ***ICML'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=9592355331559392684&hl=en&as_sdt=0,5)].
  - Minimax entropy principle and its application to texture modeling - ***Neural Computing***, 1997. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=407872717119429940)].
  - Parameter Expansion for Data Augmentation - ***Journal of the American Statistical Association***, 1999. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=15342818142955984734)].
  - Image segmentation by data-driven markov chain monte carlo - ***IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=3461400072144667491&hl=en&as_sdt=0,5)]. Classic method for image segmentation via generative modeling.
  - Efficient Learning of Sparse Representations with an Energy-Based Model - ***NeurIPS'06***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=2247668190782691760&hl=en&as_sdt=0,5)].
  - A Tutorial on Energy-Based Learning - ***Predicting Structured Data, MIT Press***, 2006. [[All Versiosn](https://scholar.google.com/scholar?cluster=8819502341081664768&hl=en&as_sdt=0,5)]. Yann LeCun's tutorial on energy-based learning.
  - Unsupervised Representaton Learning with Deep Convolutional Generative Adversarial Networks - ***ICLR'16***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=3321343160055675528&hl=en&as_sdt=0,5)].
  - Analysis of Langevin Monte Carlo via Convex Optimization - ***Journal of Machine Learning Research***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=5305860199396047317&hl=en&as_sdt=0,5)].
  - A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs - ***Science***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=1478382321633671444&hl=en&as_sdt=0,5)].
  - Where do hypotheses come from? - ***Cognitive Psychology***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=17480320046655923235&hl=en&as_sdt=2005&sciodt=0,5)]. A Bayesian account for modeling basic rules as the hypothesis space.
  - A Bayesian Analysis of Some Non-parametric Problems - ***The Annals of Statistics***, 1973. [[All Versions](https://scholar.google.com/scholar?cluster=3969163427460060902&hl=en&as_sdt=0,5)]. A classic review on non-parametric problems.
  - Mixtures of Dirichlet Process with Applications to Bayesian Nonparametric Problems - ***The Annals of Statistics***, 1974. [[All Versions](https://scholar.google.com/scholar?cluster=17937202534282344046&hl=en&as_sdt=0,5)]. The original paper on Dirichlet Process modeling for non-parametric problems.
  - Latent Semantic Indexing: A Probabilistic Analysis - ***Journal of Computer and System Sciences***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=7296120469860429813&hl=en&as_sdt=0,5)]. The original paper on hierarchical topic model.
  - Nonparametric Bayesian Data Analysis - ***Statistical Science***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=13476170780072319995&hl=en&as_sdt=0,5)].
  - Hierarchical topic models and the nested Chinese restaurant process - ***NeurIPS'03***, 2003. [[All Versions](https://scholar.google.com/scholar?cluster=15040818675282958700&hl=en&as_sdt=0,5)]. The original paper for nested Chinese restaurant process.
  - Learning Systems of Concepts with an Infinite Relational Model - ***AAAI'06***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3207350432755252565&hl=en&as_sdt=0,5)].
  - The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies - ***Journal of the ACM***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=8216933258869737505&hl=en&as_sdt=0,5)].
  - Infinite Latent Feature Models and the Indian Buffet Process - ***Gatsby Computational Neuroscience Unit Technical Report 2005-001***, 2005. [[All Versions](https://scholar.google.com/scholar?cluster=13180738480564152907&hl=en&as_sdt=0,5)].
  - The Indian Buffet Process: An Introduction and Review - ***Journal of Machine Learning Research***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=6301314251995890943&hl=en&as_sdt=0,5)]. Tom Griffiths and Zoubin Ghahramani's review on infinite models, including the Chinese Restaurant Process (CRP) and the Indian Buffet Process (IBP).
  - Nonparametric Bayesian Logic - ***UAI'05***, 2005. [[All Versions](https://scholar.google.com/scholar?cluster=18267211625980322095&hl=en&as_sdt=0,5)]. The first paper integrating logic into non-parametric model.
  - Infinite Hidden Relational Models - ***UAI'06***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=2143172296528388141&hl=en&as_sdt=2005&sciodt=0,5)].
  - Statistical Predicate Invention - ***ICML'07***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=17009312281859401704&hl=en&as_sdt=2005&sciodt=0,5)]. Treating predicate invention as a non-parametric problem, in the account of statistics.
  - A Tutorial on Bayesian Optimization - 2018. [[All Versions](https://scholar.google.com/scholar?cluster=7971934771645047583&hl=en&as_sdt=0,5)].
  - Practical Bayesian Optimization of Machine Learning Algorithms - ***NeurIPS'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=14442949298925775705&hl=en&as_sdt=0,5)]. The original paper for applying Bayesian optimization to machine learning hyperparameter selection.
  - Taking the Human Out of the Loop: A Review of Bayesian Optimization - ***Proceedings of the IEEE***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=2039456143890648437&hl=en&as_sdt=0,5)].
  - Generative Modeling Explained - ***Statistical Machine Learning Tutorials***, 2022. This tutorial on generative modeling is in part of Statistical Machine Learning Tutorial by Ying Nian Wu at UCLA Statistics. The tutorial goes over the key equations and algorithms for learning recent generative models, including energy-based models, diffusion/score-based models, autoregressive/flow-based models, VAEs, and GANs, and explains the connections between these models.
  - Finding scientific topics - ***Proceedings of the National Academy of Sciences***, 2004. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=17382767110929995134)]. Application on scientific paper ananlysis for hierarchical topic model.
  - The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies - ***Journal of the ACM***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=8216933258869737505&hl=en&as_sdt=0,5)].
- Complexity & Information Theory
  - Visual Pattern Discrimination - ***IRE Transactions on Information Theory***, 1962. [[All Versions](https://scholar.google.com/scholar?cluster=10729525966103382864)].
  - Algorithmic Information Theory - ***IBM Journal of Research and Development***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=14735710867906424793&hl=en&as_sdt=0,5)]. Chaitin's original paper on Algorithmic Information Theory.
  - Reducing the dimensionality of data with neural networks - ***Science***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=15344645275208957628&hl=en&as_sdt=0,5)]. The original paper on Variational Autoencoder.
  - Representation Learning: A Statistical Perspective - ***Annual Review of Statistics and Its Application***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=14358027809538175293)]. Song-Chun Zhu and Ying Nian Wu's review on representation learning, in an account of statistics.
  - On the information bottleneck theory of deep learning - ***Journal of Statistical Mechanics: Theory and Experiment***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=12271240925674881982)].
  - Image complexity and spatial information - ***International Workshop on Quality of Multimedia Experience***, 2013. [[All Versions](https://scholar.google.com/scholar?cluster=16011036229039693102)].
  - How variability shapes learning and generalization - ***Trends in Cognitive Sciences***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=10940775338620708972)]. A comprehensive review on the trade-off between variability and generalization ability.
  - Show or tell? Exploring when (and why) teaching with language outperforms demonstration - ***Cognition***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11837154580063293174&hl=en&as_sdt=0,5)]. The findings of this paper suggest that language communicates complex concepts by directly transmitting abstract rules. In contrast, demonstrations transmit examples, requiring the learner to infer the rules.
  - A Mathematical Theory of Communication - ***The Bell System Technical Journal***, 1948. [[All Versions](https://scholar.google.com/scholar?cluster=8313213127749369813)]. Shannon's original paper on Information Theory.
  - An introduction to Kolmogorov complexity and its applications - ***Springer***, 2008. [[All Versions](https://scholar.google.com/scholar?cluster=8746708322477453221)]. The introductory book for Algorithmic Information Theory, especially the Kolmogorov complexity theory.
  - Complexity and the representation of patterned sequences of symbols - ***Psychological Review***, 1972. [[All Versions](https://scholar.google.com/scholar?cluster=3426861135318645138)]. Herbert Simon's review on subjective complexity.
  - From Algorithmic to Subjective Randomness - ***NeurIPS'03***, 2003. [[All Versions](https://scholar.google.com/scholar?cluster=14721764738308036578)].
  - On the Complexity of Bayesian Generalization - ***ICML'23***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=5817813824878811147)]. [[Code](https://github.com/SHI-Yu-Zhe/bayesian-generalization-complexity)]. [[Models](https://drive.google.com/file/d/1eCuFqBYN8kuiAmoVtXWedXW0r0TdY55W/view)]. A concept complexity account for rule- and similarity-based Bayesian concept generalization.
  - A global geometric framework for nonlinear dimensionality reduction - ***Science***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=14602426245887619907)]. The original paper on spectrum clustering.
  - Deep Learning and the Information Bottleneck Principle - ***IEEE Information Theory Workshop'15***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=13152354842433826281)]. The first paper identifying the problem of information bottleneck in representation learning.
  - Visual complexity: a review - ***Psychological Bulletin***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=10747901143387624939)]. [[APA](https://psycnet.apa.org/record/2006-00818-005)]. A psychological account on visual complexity.
  - Seeing and speaking: How verbal “description length” encodes visual complexity - ***Journal of Experimental Psychology***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=246820603191585233)]. [[APA](https://psycnet.apa.org/record/2021-83037-001)]. Empirical evidencs showing the relation between visual complexity and description length.
  - Identifying concept libraries from language about object structure - ***CogSci'22***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4019205027627496528)].
  - Algorithmic Information Theory - ***IBM Journal of Research and Development***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=14735710867906424793)]. Chaitin's original paper on Algorithmic Information Theory.
- Domain Specific Language
  - The complete guide to (external) Domain Specific Languages
  - Action Languages, Answer Sets, and Planning - ***The Logic Programming Paradigms***, 1999. [[All Versions](https://scholar.google.com/scholar?cluster=2045126541850245645&hl=en&as_sdt=0,5)].
  - Design Guidelines for Domain Specific Languages - ***OOPSLA Workshop on Domain-Specific Modeling (DSM' 09)***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=1962567819031018744)]. Guidelines to support a DSL developer to achieve better quality of the language design and a better acceptance among its users.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - The BioPAX community standard for pathway data sharing - ***Nature Biotechnology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11368332679628594895)]. [[Preprint](https://core.ac.uk/download/pdf/216139091.pdf)]. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - Learning the language of viral evolution and escape - ***Science***, 2021. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=13862653184613223515)]. Natural language processing with two components: grammar (or syntax) and meaning (or semantics) for predicting which viral mutations may lead to viral escape.
  - Domain-Specific Language - ***Wikipedia***. Wikipedia encyclopedia entry on Domain Specific Languages.
  - The BioPAX community standard for pathway data sharing - ***Nature Biotechnology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11368332679628594895)]. [[Preprint](https://core.ac.uk/download/pdf/216139091.pdf)]. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - The BioPAX community standard for pathway data sharing - ***Nature Biotechnology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11368332679628594895)]. [[Preprint](https://core.ac.uk/download/pdf/216139091.pdf)]. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming - ***ICML'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=2892523061439714130)]. This work presents PClean, a probabilistic programming language (PPL) for leveraging dataset-specific knowledge to automate Bayesian cleaning, automating Bayesian approaches given the diversity of real-world error patterns and the hardness of inference.
  - Domain Engineering - ***Wikipedia***. Wikipedia encyclopedia entry on Domain Engineering.
  - Grammar Prompting for Domain-Specific Language Generation with Large Language Models - ***NeurIPS'23***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11694070042468483715)]. Grammar prompting is a simple approach to enable LLMs to use external knowledge and domain-specific constraints expressed through a grammar in Backus--Naur Form (BNF) during in-context learning.
  - Comparison of multi-paradigm programming languages - ***Wikipedia***. Programming languages may support multiple programming paradigms. This Wikipedia encyclopedia entry lists a concise reference for the programming paradigms.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - KnitScript: A Domain-Specific Scripting Language for Advanced Machine Knitting - ***UIST'23***, 2023. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=KnitScript%3A+A+Domain-Specific+Scripting+Language+for+Advanced+Machine+Knitting&btnG=)]. [[Project](https://pypi.org/project/knit-script/)]. This paper presents KnitScript, a domain-specific machine knitting scripting language that supports computationally driven knitting designs. KnitScript provides a comprehensive virtual model of knitting machines, giving access to machine-level capabilities as they are needed while automating a variety of tedious and error-prone details.
  - A domain‑specifc language framework for farm management information systems in precision agriculture - ***Precision Agriculture***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1495954486695213496)]. This paper proposes a domain-specific language framework for the design and development of precision-agriculture FMISs, which copes with challenges on supporting the understandability, enhancing communication and analysis of the design decisions, and the communication among stakeholders.
  - Corel: A DSL for Cooking Recipes - 2021. [[All Versions](https://scholar.google.com/scholar?cluster=9477049800574267813)]. [[Corel recipe page](https://roorda.dev/recipes/0)]. [[International Network of Food Data Systems (INFOODS)](https://www.fao.org/infoods/infoods/tables-and-databases/faoinfoods-databases/en/)]. The Corel DSL for cooking recipes enables understanding of and computation with ingredients, and can construct a nutrition label for the recipe.
  - The BioPAX community standard for pathway data sharing - ***Nature Biotechnology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11368332679628594895)]. [[Preprint](https://core.ac.uk/download/pdf/216139091.pdf)]. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks.
  - Domain Specific Language for Smart Contract Development - ***ICBC'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=16998538751745390273)]. [[Preprint](http://eprints-dev5.cs.univie.ac.at/6341/1/PID6382125.pdf)]. This research addresses the understanding hardness raised from the conceptual discrepancy between contractual clauses and corresponding code of the Solidity programming language, by the design and study of a domain-specific smart contract language based on higher level of abstraction that can be automatically transformed to an implementation.
  - A Language for Counterfactual Generative Models - ***ICML'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=2067748786482591497)]. [[Project](https://github.com/zenna/Omega.jl)]. This paper presents Omega, a probabilistic programming language with support for counterfactual inference. This feature is accomplished by introducing a new operator to probabilistic programming akin to Pearl’s do.
  - Situation Calculus - ***Wikipedia***. Wikipedia on Situation Calculus, a logic formalism designed for representing and reasoning about dynamical domains.
  - What is Answer Set Programming? - ***AAAI'08***, 2008. [[All Versions](https://scholar.google.com/scholar?cluster=3691841207891991771)]. Lifschitz's tutorial on Answer Set Programming.
  - Qualitative Simulation - ***Artificial Intelligence***, 1986. [[All Versions](https://scholar.google.com/scholar?cluster=4945009733425184345&hl=en&as_sdt=0,5)]. Benjamin Kuipers' original paper on qualitative reasoning.
  - Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge - ***MIT Press***, 1994. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=6634684154722677465)]. Benjamin Kuipers' comprehensive book on qualitative reasoning.
  - Qualitative and quantitative simulation: bridging the gap - ***Artificial Intelligence***, 1997. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=9033452473914228535)].
  - A Logic Programming Language for Computational Nucleic Acid Devices - ***ACS Synthetic Biology***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=3336951672389047784)]. This paper presents a logic programming language that allows a broad range of computational nucleic acid systems to be designed and analyzed. The language extends standard logic programming with a novel equational theory to express nucleic acid molecular motifs. It automatically identifies matching motifs present in the full system, in order to apply a specified transformation expressed as a logical rule.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - A domain‑specifc language framework for farm management information systems in precision agriculture - ***Precision Agriculture***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1495954486695213496)]. This paper proposes a domain-specific language framework for the design and development of precision-agriculture FMISs, which copes with challenges on supporting the understandability, enhancing communication and analysis of the design decisions, and the communication among stakeholders.
  - The BioPAX community standard for pathway data sharing - ***Nature Biotechnology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11368332679628594895)]. [[Preprint](https://core.ac.uk/download/pdf/216139091.pdf)]. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - Errors are Useful Prompts: Instruction Guided Task Programming with Verifier-Assisted Iterative Prompting - 2023. [[All Versions](https://scholar.google.com/scholar?cluster=8063693456660536915)]. [[Code](https://github.com/ac-rad/xdl-generation)]. [[Website](https://ac-rad.github.io/clairify/)]. This paper proposes CLAIRIFY, an approach that combines automatic iterative prompting with program verification to ensure programs written in data-scarce domain-specific language are syntactically valid and incorporate environment constraints.
  - No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization - ***IEEE Transactions on Visualization and Computer Graphics***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=17206818917381447796&hl=en&as_sdt=0,5)]. A survey on the design and implementation of 57 JSON-style DSLs for a variety of visualization and visual interaction tasks, suggesting that no one DSL will be able to capture all of them without compromising essential parts of its domain design.
  - Usability Evaluation of Domain-Specific Languages - ***ICQICT'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=3047215455890195199)]. An initiative arguing that a systematic approach based on User Interface experimental validation techniques should be used to assess the impact of new DSLs.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - A domain‑specifc language framework for farm management information systems in precision agriculture - ***Precision Agriculture***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1495954486695213496)]. This paper proposes a domain-specific language framework for the design and development of precision-agriculture FMISs, which copes with challenges on supporting the understandability, enhancing communication and analysis of the design decisions, and the communication among stakeholders.
  - The BioPAX community standard for pathway data sharing - ***Nature Biotechnology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11368332679628594895)]. [[Preprint](https://core.ac.uk/download/pdf/216139091.pdf)]. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks.
  - iContractML 2.0: A domain-specific language for modeling and deploying smart contracts onto multiple blockchain platforms - ***Information and Software Technology***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=1548144959305241494&hl=en&as_sdt=0,5)]. A reference model and platform agnostic language for smart contracts.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - A domain‑specifc language framework for farm management information systems in precision agriculture - ***Precision Agriculture***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1495954486695213496)]. This paper proposes a domain-specific language framework for the design and development of precision-agriculture FMISs, which copes with challenges on supporting the understandability, enhancing communication and analysis of the design decisions, and the communication among stakeholders.
  - The BioPAX community standard for pathway data sharing - ***Nature Biotechnology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11368332679628594895)]. [[Preprint](https://core.ac.uk/download/pdf/216139091.pdf)]. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks.
  - Domain-Specific Languages - ***Pearson Education***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=3653365103385845410)]. [[Domain-Specific Languages Guide](https://martinfowler.com/dsl.html)]. When carefully selected and used, Domain-Specific Languages (DSLs) may simplify complex code, promote effective communication with customers, improve productivity, and unclog development bottlenecks. In Domain-Specific Languages, noted software development expert Martin Fowler first provides the information software professionals need to decide if and when to utilize DSLs. Then, where DSLs prove suitable, Fowler presents effective techniques for building them, and guides software engineers in choosing the right approaches for their applications.
  - When and How to Develop Domain-Specific Languages - ***ACM Computing Surveys***, 2005. [[All Versions](https://scholar.google.com/scholar?cluster=8598236436890577027)]. A review on DSL development methodologies that identify patterns in the decision, analysis, design, and implementation phases of DSL development.
  - Domain-specific languages: an annotated bibliography - ***ACM SIGPLAN Notices***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=8845429548327315750)]. A survey on the topic of domain-specific languages as used for the construction and maintenance of software systems.
  - Communicating Natural Programs to Humans and Machines - ***NeurIPS'22***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13381039702346039142)]. While humans readily generate and interpret instructions in a general language, computer systems are shackled to a narrow domain-specific language that they can precisely execute. This makes building intelligent systems that can generalize to novel situations such as ARC difficult. Human-generated instructions are referred as `natural programs'. While they resemble computer programs, they are distinct in two ways: First, they contain a wide range of primitives; Second, they frequently leverage communicative strategies beyond directly executable codes.
  - The BioPAX community standard for pathway data sharing - ***Nature Biotechnology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11368332679628594895)]. [[Preprint](https://core.ac.uk/download/pdf/216139091.pdf)]. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks.
  - A high-level programming language for generative protein design - 2022. [[All Versions](https://scholar.google.com/scholar?cluster=11732741354610784314)]. A high-level programming language based on modular building blocks that allows a designer to easily compose a set of desired properties. Along with the programming language, there is an energy-based generative model, built on atomic resolution structure prediction with a language model, that realizes all-atom structure designs that have the programmed properties.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - Infinite Photorealistic Worlds Using Procedural Generation - ***CVPR'23***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11620922717915489091)]. [[Website](https://infinigen.org/)]. [[Supplementary Text](https://openaccess.thecvf.com/content/CVPR2023/supplemental/Raistrick_Infinite_Photorealistic_Worlds_CVPR_2023_supplemental.pdf)]. This paper introduces Infinigen, a procedural generator of photorealistic 3D scenes of the natural world. Infinigen is entirely procedural: every asset, from shape to texture, is generated from scratch via randomized mathematical rules, using no external source and allowing infinite variation and composition.
  - Learning to Infer Graphics Programs from Hand-Drawn Images - ***NeurIPS'18***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=14065112485794121024)]. The method learns a model that uses program synthesis techniques to recover a graphics program from drawing primitives. These programs have constructs like variable bindings, iterative loops, or simple kinds of conditionals. With a graphics program in hand, we can correct errors made by the deep network and extrapolate drawings.
  - DreamCoder: growing generalizable, interpretable knowledge with wake–sleep Bayesian program learning - ***Philosophical Transactions of the Royal Society A***, 2023. [[All Versions](https://scholar.google.com/scholar?cluster=11356436337624711843)]. [[Preprint](https://arxiv.org/abs/2006.08381)]. This paper presents DreamCoder, a system that learns to solve problems by writing programs. It builds expertise by creating domain-specific programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages. A ‘wake–sleep’ learning algorithm alternately extends the language with new symbolic abstractions and trains the neural network on imagined and replayed problems. DreamCoder solves both classic inductive programming tasks and creative tasks such as drawing pictures and building scenes.
  - No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization - ***IEEE Transactions on Visualization and Computer Graphics***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=17206818917381447796)]. A survey on the design and implementation of 57 JSON-style DSLs for a variety of visualization and visual interaction tasks, suggesting that no one DSL will be able to capture all of them without compromising essential parts of its domain design.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - OpenLaw - ***OpenLaw.io***. It is now possible to model all or parts of legal agreements using code (smart contracts), decreasing the cost and friction of creating, securing, and generating binding legal agreements. Lawyers lack basic tools to build these dynamic, “smart” contracts in a way that is enforceable and understandable to a legal professional. OpenLaw is a technology stack to help power next generation "smart" legal agreements, with a domain-specific markup language, a integration framework, and a series of general applications.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - A domain‑specifc language framework for farm management information systems in precision agriculture - ***Precision Agriculture***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1495954486695213496)]. This paper proposes a domain-specific language framework for the design and development of precision-agriculture FMISs, which copes with challenges on supporting the understandability, enhancing communication and analysis of the design decisions, and the communication among stakeholders.
  - The BioPAX community standard for pathway data sharing - ***Nature Biotechnology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11368332679628594895)]. [[Preprint](https://core.ac.uk/download/pdf/216139091.pdf)]. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - The BioPAX community standard for pathway data sharing - ***Nature Biotechnology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11368332679628594895)]. [[Preprint](https://core.ac.uk/download/pdf/216139091.pdf)]. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - The BioPAX community standard for pathway data sharing - ***Nature Biotechnology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11368332679628594895)]. [[Preprint](https://core.ac.uk/download/pdf/216139091.pdf)]. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - The BioPAX community standard for pathway data sharing - ***Nature Biotechnology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11368332679628594895)]. [[Preprint](https://core.ac.uk/download/pdf/216139091.pdf)]. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - A domain‑specifc language framework for farm management information systems in precision agriculture - ***Precision Agriculture***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1495954486695213496)]. This paper proposes a domain-specific language framework for the design and development of precision-agriculture FMISs, which copes with challenges on supporting the understandability, enhancing communication and analysis of the design decisions, and the communication among stakeholders.
  - The BioPAX community standard for pathway data sharing - ***Nature Biotechnology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11368332679628594895)]. [[Preprint](https://core.ac.uk/download/pdf/216139091.pdf)]. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - A domain‑specifc language framework for farm management information systems in precision agriculture - ***Precision Agriculture***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1495954486695213496)]. This paper proposes a domain-specific language framework for the design and development of precision-agriculture FMISs, which copes with challenges on supporting the understandability, enhancing communication and analysis of the design decisions, and the communication among stakeholders.
  - The BioPAX community standard for pathway data sharing - ***Nature Biotechnology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11368332679628594895)]. [[Preprint](https://core.ac.uk/download/pdf/216139091.pdf)]. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - A domain‑specifc language framework for farm management information systems in precision agriculture - ***Precision Agriculture***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1495954486695213496)]. This paper proposes a domain-specific language framework for the design and development of precision-agriculture FMISs, which copes with challenges on supporting the understandability, enhancing communication and analysis of the design decisions, and the communication among stakeholders.
  - The BioPAX community standard for pathway data sharing - ***Nature Biotechnology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11368332679628594895)]. [[Preprint](https://core.ac.uk/download/pdf/216139091.pdf)]. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks.
  - Universal chemical programming language for robotic synthesis repeatability - ***Nature Synthesis***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=3455106495990439366)]. [[Preprint](https://www.chem.gla.ac.uk/cronin/images/pubs/rauschen-natsynthesisjan24.pdf)]. This paper presents an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions, including reductive amination, ring formation, esterification, carbon–carbon bond formation and amide coupling on four different hardware systems in two laboratories. With around 50 lines of code per reaction, the approach uses abstraction to efficiently compress chemical protocols.
  - Scenic: a language for scenario specification and data generation - ***Machine Learning***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=13790565080942515865)]. Thie paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships with the ability to declaratively impose hard and soft constraints over the scenario.
  - A domain‑specifc language framework for farm management information systems in precision agriculture - ***Precision Agriculture***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1495954486695213496)]. This paper proposes a domain-specific language framework for the design and development of precision-agriculture FMISs, which copes with challenges on supporting the understandability, enhancing communication and analysis of the design decisions, and the communication among stakeholders.
  - The BioPAX community standard for pathway data sharing - ***Nature Biotechnology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=11368332679628594895)]. [[Preprint](https://core.ac.uk/download/pdf/216139091.pdf)]. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks.
- Analogy
  - Metaphor - ***Plato Stanford***. A computational philosophy account on Metaphor, a poetically or rhetorically ambitious use of words, a figurative as opposed to literal use.
  - Analogy and Analogical Reasoning - ***Plato Stanford***. A computational philosophy account on Analogy, a comparison between two objects, or systems of objects, that highlights respects in which they are thought to be similar.
  - A Cognitive Theory of Metaphor - ***MIT Press***, 1985. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=a+cognitive+theory+of+metaphor&btnG=)]. A cognitive account on Metaphor.
  - The structure-mapping engine: Algorithm and examples - ***Artificial Intelligence***, 1989. [[All Versions](https://scholar.google.com/scholar?cluster=16104901325436513899&hl=en&as_sdt=0,5)]. A computational implementation of analogy.
  - Structure mapping in analogy and similarity - ***American Psychologist***, 1997. [[All Versions](https://scholar.google.com/scholar?cluster=3497411606978611830&hl=en&as_sdt=0,5)]. A perspective unifying analogy and similarity judgement.
  - A theory of relation learning and cross-domain generalization - ***Psychological Review***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=8559821723107269122&hl=en&as_sdt=0,5)]. A comprehensive review on the perspective of treating analogy as cross-domain generalization.
  - Emergence of analogy from relation learning - ***Proceedings of the National Academy of Sciences***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4877125748339538047&hl=en&as_sdt=0,5)]. Analogy feature in language models.
  - Analogies Explained: Towards Understanding Word Embeddings - ***ICML'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15445529659618849253&hl=en&as_sdt=0,5)]. Explaining the analogy capability in word embeddings.
  - Generalize and Blend: Concept Blending Based on Generalization, Analogy, and Amalgams - ***ICCC'15***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=11073359237116879862&hl=en&as_sdt=0,5)].
  - Analogy-preserving Semantic Embedding for Visual Object Categorization - ***ICML'13***, 2013. [[All Versions](https://scholar.google.com/scholar?cluster=9332855910734484101&hl=en&as_sdt=0,5)]. The first application of analogy to machine learning.
  - VISALOGY: Answering Visual Analogy Questions - ***NeurIPS'15***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=7665427758655324654&hl=en&as_sdt=0,5)].
  - Detecting Unseen Visual Relations Using Analogies - ***CVPR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=16686853801653819556&hl=en&as_sdt=0,5)].
  - Analogy between concepts - ***Artificial Intelligence***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=1397905953174123757&hl=en&as_sdt=0,5)]. A mathematical account on analogy.
  - Learning to Make Analogies by Contrasting Abstract Relational Structure - ***ICLR'19***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=15521573039503233138&hl=en&as_sdt=0,5)].
  - Sky + Fire = Sunset. Exploring Parallels between Visually Grounded Metaphors and Image Classifiers - ***ACL'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=5747285277687442001&hl=en&as_sdt=0,5)].
  - Analogy as Nonparametric Bayesian Inference over Relational Systems - ***CogSci'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=1798148167130120057&hl=en&as_sdt=0,5)].
  - Visual Analogy: Deep Learning Versus Compositional Models - ***CogSci'21***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=1187822306970312749&hl=en&as_sdt=0,5)]. A human-deep-learning comparison on similarity judgement.
  - Preschoolers and adults make inferences from novel metaphors - ***CogSci'22***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=16038983545360341739&hl=en&as_sdt=0,44)]. A piece of evidence that understanding metaphors is capable for different cognitive development phases.
  - Similarity involving attributes and relations: Judgments of similarity and difference are not inverses - ***Psychological Science***, 1990. [[All Versions](https://scholar.google.com/scholar?cluster=13205938250772079784&hl=en&as_sdt=0,5)].
  - Skip-Gram − Zipf + Uniform = Vector Additivity - ***ACL'17***, 2017. [[All Versions](https://scholar.google.com/scholar?cluster=11732363456979525246&hl=en&as_sdt=0,5)].
- Causality
  - Causality - ***Wikipedia***. Wikipedia on causality, which is influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause.
  - Causal Models - ***Plato Stanford***. A computational philosophy account on Causal models, which are mathematical models representing causal relationships within an individual system or population.
  - Causal Theories of Mental Content - ***Plato Stanford***. A computational philosophy account on causal theories of mental content, which attempts to explain how thoughts can be about things.
  - Predictive and Diagnostic Learning Within Causal Models: Asymmetries in Cue Competition - ***Journal of Experimental Psychology***, 1992. [[All Versions](https://scholar.google.com/scholar?cluster=9614241045842043939&hl=en&as_sdt=0,5)]. Experimental evidences for distincting causality and association.
  - Causal Reasoning - ***The Oxford Handbook of Cognitive Psychology***, 2013. [[All Versions](https://scholar.google.com/scholar?cluster=11361740093816709089&hl=en&as_sdt=0,5)].
  - Reasoning with cause and effect - 1998. Judea Pearl's tutorials on causal reasoning with operations on Bayesian networks.
  - The Seven Tools of Causal Inference, with Reflections on Machine Learning - ***Communications of the ACM***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=13296019510897277617&hl=en&as_sdt=0,5)]. Judea Pearl's review on causal inference in probabilistic graph models.
  - Toward Causal Representation Learning - ***Proceedings of the IEEE***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=15629454810797806102&hl=en&as_sdt=0,5)]. Yoshua Bengio's review on the perspective of treating causal inference as a representation learning problem.
  - Theory-Based Causal Induction - ***Psychological Review***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=13980129728092173387&hl=en&as_sdt=0,5)]. Thomas Griffiths' review on causal Bayesian theory induction.
  - Theory-Based Causal Transfer: Integrating Instance-Level Induction and Abstract-Level Structure Learning - ***AAAI'20***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=9411622427165139667&hl=en&as_sdt=0,5)]. A computatinoal account on causal transfer.
  - Inferring causal networks from observations and interventions - ***Cognitive Science***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=12050301037347772984&hl=en&as_sdt=2005&sciodt=0,5)].
  - Constraints on Hypothesis Selection in Causal Learning - ***CogSci'15***, 2015. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=16920774374067505248&scipsc=&q=Constraints+on+hypothesis+selection+in+causal+learning&btnG=)].
  - Eye-tracking causality - ***Psychological Science***, 2017. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=17518200401109470519)].
  - What happened? Reconstructing the past through vision and sound - 2021. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=12975579257004398798)].
  - Causal Reasoning in Rats - ***Science***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=17987039255457850949&hl=en&as_sdt=0,5)]. A piece of evidence for the capability of causal reasoning in intelligent animals.
  - Do New Caledonian crows solve physical problems through causal reasoning? - ***Proceedings of the Royal Society B: Biological Sciences***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=18374985546068164189&hl=en&as_sdt=0,5)]. A piece of evidence for the capability of causal reasoning in intelligent animals.
  - Do six-month-old infants perceive causality? - ***Cognition***, 1987. [[All Versions](https://scholar.google.com/scholar?cluster=14270905342434182186&hl=en&as_sdt=0,5)].
  - How do people generalize causal relations over objects? A non-parametric Bayesian account - 2021. [[All Versions](https://scholar.google.com/scholar?cluster=9078127785707706032&hl=en&as_sdt=0,5)].
  - Identification of Causal Effects Using Instrumental Variables - ***Journal of the American Statistical Association***, 1996. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=17166265099721941605)].
  - Causal Reasoning - ***The Oxford Handbook of Cognitive Psychology***, 2013. [[All Versions](https://scholar.google.com/scholar?cluster=11361740093816709089&hl=en&as_sdt=0,5)].
Institute & Researcher
- MIT
  - Center for Brains, Minds and Machines (CBMM) - ***MIT***.
  - Josh Tenenbaum - ***Department of Brain and Cognitive Sciences, CSAIL, MIT***, [Computational Cognitive Science Group (CoCoSci Group)](https://cocosci.mit.edu/) - ***MIT***.
  - Laura Schulz - ***Department of Brain and Cognitive Sciences, MIT***, [Early Childhood Cognition Lab](https://eccl.mit.edu/) - ***MIT***.
  - Leslie Kaelbling - ***Department of Electrical Engineering and Computer Science, CSAIL, MIT***, [The Learning & Intelligent Systems Group](https://lis.csail.mit.edu/) - ***MIT***.
  - Armando Solar-Lezama - ***Department of Electrical Engineering and Computer Science, CSAIL, MIT***, [Computer-Aided Programming Group](http://groups.csail.mit.edu/cap/) - ***MIT***.
  - Rebecca Saxe - ***Department of Brain and Cognitive Sciences, MIT***, [Social Cognitive Neuroscience Laboratory (SaxeLab)](https://saxelab.mit.edu/) - ***MIT***.
- Stanford
  - Li Fei-Fei - ***Computer Science Department, Human-Centered AI Institute, Stanford***, [Stanford Vision and Learning Lab](https://svl.stanford.edu/) - ***Stanford***.
  - Noah Goodman - ***Department of Psychology, Computer Science Department, Stanford***, [Computation & Cognition Lab (CoCoLab)](https://cocolab.stanford.edu/) - ***Stanford***.
  - Michael Frank - ***Department of Psychology, Stanford***, [The Stanford Language and Cognition Lab](http://langcog.stanford.edu/) - ***Stanford***.
  - Tobias Gerstenberg - ***Department of Psychology, Stanford***, [Causality in Cognition Lab (CICL)](https://cicl.stanford.edu/) - ***Stanford***.
  - Chelsea Finn - ***Computer Science Department, Stanford***, [Intelligence through Robotic Interaction at Scale (IRIS Group)](https://irislab.stanford.edu/) - ***Stanford***.
  - Jeremy Bailenson - ***Department of Communication, Stanford***, [Virtual Human Interaction Lab (VHIL)](https://stanfordvr.com/) - ***Stanford***.
  - Jiajun Wu - ***Computer Science Department, Stanford***.
  - Judith Fan - ***Department of Psychology, Stanford***, [Cognitive Tools Lab](https://cogtoolslab.github.io/) - ***Stanford***.
- Princeton
  - Tania Lombrozo - ***Department of Psychology, Princeton***, [Concepts & Cognition Lab](https://cognition.princeton.edu/) - ***Princeton***.
  - Thomas Griffiths - ***Department of Psychology, Department of Computer Science, Princeton***, [Computational Cognitive Science Lab](https://cocosci.princeton.edu/index.php) - ***Princeton***.
- Harvard
  - Elizabeth Spelke - ***Department of Psychology, Harvard***, [Harvard Laboratory for Developmental Studies](https://www.harvardlds.org/) - ***Harvard***.
  - Tomer Ullman - ***Department of Psychology, Harvard***, [Computation, Cognition, and Development Lab (CoCoDev)](https://cocodev.fas.harvard.edu/) - ***Harvard***.
  - Samuel Gershman - ***Department of Psychology, Harvard***, [Computational Cognitive Neuroscience Lab (CCN Lab)](https://gershmanlab.com/) - ***Harvard***.
  - Fiery Cushman - ***Department of Psychology, Harvard***, [Moral Psychology Research Lab](https://cushmanlab.fas.harvard.edu/) - ***Harvard***.
- UCLA
  - Center for Vision, Cognition, Learning and Autonomy (VCLA) - ***Department of Statistics, UCLA***.
  - Ying Nian Wu - ***Department of Statistics, UCLA***.
  - Tao Gao - ***Department of Statistics, Department of Psychology, UCLA***, [Visual Intelligence Lab](http://www.stat.ucla.edu/~taogao/index.html) - ***UCLA***.
  - Hongjing Lu - ***Department of Psychology, Department of Statistics, UCLA***, [Computational Vision and Learning Lab (CVL)](http://cvl.psych.ucla.edu/) - ***UCLA***.
  - Guy Van den Broeck - ***Department of Computer Science, UCLA***, [StarAI Lab](http://starai.cs.ucla.edu/#) - ***UCLA***.
- UC Berkeley
  - Anca Dragan - ***Department of Electrical Engineering and Computer Science, UC Berkeley***, [Interactive Autonomy and Collaborative Technologies Laboratory (InterACT)](http://interact.berkeley.edu/) - ***UC Berkeley***.
  - Fei Xu - ***Department of Psychology, UC Berkeley***, [Berkeley Early Learning Lab (Xu Lab)](https://babylab5.wixsite.com/bell) - ***UC Berkeley***.
  - Alison Gopnik - ***Department of Psychology, UC Berkeley***, [Cognitive Development & Learning Lab (Gopnik Lab)](http://www.gopniklab.berkeley.edu/) - ***UC Berkeley***.
  - Steve Piantadosi - ***Department of Psychology, UC Berkeley***, [The computation and language lab (colala)](http://colala.berkeley.edu/) - ***UC Berkeley***.
  - Celeste Kidd - ***Department of Psychology, UC Berkeley***, [Kidd Lab](https://www.kiddlab.com/) - ***UC Berkeley***.
  - Celeste Kidd - ***Department of Psychology, UC Berkeley***, [Kidd Lab](https://www.kiddlab.com/) - ***UC Berkeley***.
  - Celeste Kidd - ***Department of Psychology, UC Berkeley***, [Kidd Lab](https://www.kiddlab.com/) - ***UC Berkeley***.
- BNU
  - Yanchao Bi - ***IDG/McGovern Institute for Brain Research and the State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University (BNU)***, [Yanchao Bi's Concept Lab (Bi Lab)](http://bilab.bnu.edu.cn/) - ***BNU***.
- PKU
  - Song-Chun Zhu - ***School of AI and Institute for AI, Peking University (PKU)***.
  - Yixin Zhu - ***School of AI and Institute for AI, Peking University (PKU)***, [Cognitive Reasoning Lab (CoRe Lab)](https://pku.ai/) - ***PKU***.
- UCSD
  - Zhuowen Tu - ***Department of Computer Science, UCSD***, [Machine Learning, Perception, and Cognition Lab (mlPC)](https://pages.ucsd.edu/~ztu/Group.htm) - ***UCSD***.
  - Ed Vul - ***Department of Psychology, UCSD***, [Computational Cognition Lab](http://www.evullab.org/index.html) - ***UCSD***.
- NYU
  - Ernest Davis - ***Department of Computer Science, Courant Institute of Mathematical Sciences, NYU***.
  - Gary Marcus - ***Department of Psychology, NYU***.
  - Brenden Lake - ***Department of Psychology, NYU***, [Human & Machine Learning Lab (Lake Lab)](https://lake-lab.github.io/) - ***NYU***.
  - Todd Gureckis - ***Department of Psychology, NYU***, [Computation & Cognition Lab](http://gureckislab.org/) - ***NYU***.
  - Wei Ji Ma - ***Department of Psychology, Center for Neural Science, NYU***, [Wei Ji Ma Lab](http://www.cns.nyu.edu/malab/) - ***NYU***.
- JHU
  - Chaz Firestone - ***Department of Psychological and Brain Sciences, Johns Hopkins University (JHU)***, [Hopkins Perception & Mind Lab](https://perception.jhu.edu/) - ***JHU***.
- SIT
  - Mark Ho - ***Department of Computer Science, Stevens Institute of Technology (SIT)***, [Computation and Decision-Making Lab](https://codec-lab.github.io/) - ***SIT***.
People & Book
- Ulf Grenander
  - A Calculus of Ideas: A Mathematical Study of Thinking - ***World Scientific Publishing Company***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=12182416000849265255&hl=en&as_sdt=0,5)].
  - General Pattern Theory: A Mathematical Study of Regular Structures - ***Oxford University Press***, 1993. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=General+Pattern+Theory&btnG=)].
- Michael Tomasello
  - Origins of human communication - ***MIT Press***, 2010. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=2553369883266458474)].
  - The cultural origins of human cognition - ***Havard University Press***, 2000. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=5000469061641945144)].
- Judea Pearl
  - The Book of Why: The New Science of Cause and Effect - ***Basic Books***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=2505901292485349932&hl=en&as_sdt=0,5)].
  - Causality: Models, Reasoning and Inference - ***Cambridge University Press***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=10996260119229499611&hl=en&as_sdt=0,5&as_vis=1)].
- Daniel Kahneman
  - Thinking, fast and slow - ***Farrar Straus Giroux***, 2011. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=3255681708785115121)].
- Karl Popper
  - The logic of scientific discovery - ***Routledge***, 2005. [[All Versions](https://scholar.google.com/scholar?cluster=5836864564733788424&hl=en&as_sdt=0,5)].
  - All Life is Problem Solving - ***Routledge***, 2001. [[All Versions](https://scholar.google.com/scholar?cluster=9799073870888093350&hl=en&as_sdt=0,5)].
- John Hopcroft
  - Foundations of Data Science - ***Cambridge University Press***. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=1802704438630899850)].
  - Introduction to Automata Theory, Languages, and Computation - ***Pearson***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=326269839585842480)].
- Susan Carey
  - The Origin of Concepts - ***Oxford University Press***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=11493102398422813821&hl=en&as_sdt=0,5)].
  - Conceptual Change in Childhood - ***MIT Press***, 1985. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=conceptual+change+in+childhood+susan+carey&btnG=)].
- David Marr
  - Vision: A Computational Investigation into the Human Representation and Processing of Visual Information - ***MIT Press***, 1982. [[All Versions](https://scholar.google.com/scholar?cluster=14386368570811483142&hl=en&as_sdt=0,44)].

Ecosyste.ms: Awesome

awesome-agi-cocosci

Academic Tools

Paper Writing

Paper Reading

Literature Management

Courses

Programming

Knowledge Management

Papers

Abduction

Concepts

Communications

Problem Solving

System 1 & System 2

Explainability

Embodied Intelligence

Evolutionary Intelligence

Methodologies for Experiments

Meta-Level Considerations

Science Logology

Theory of Mind

Commonsense

Inductive Logic & Program Synthesis

Knowledge Representation

Cognitive Development

Learning in the Open World

Learning with Cognitive Plausibility

Tasks & Environments

Bayesian Modeling

Complexity & Information Theory

Domain Specific Language

Analogy

Causality

Institute & Researcher

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People & Book

Ulf Grenander

Michael Tomasello

Judea Pearl

Daniel Kahneman

Karl Popper

John Hopcroft

Susan Carey

David Marr