https://github.com/biaslab/iwai2024-ambiguity

Expected free energy minimization with approximations to nonlinear observation functions
https://github.com/biaslab/iwai2024-ambiguity

active-inference bayesian-filtering nonlinearity

Last synced: 4 months ago
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Expected free energy minimization with approximations to nonlinear observation functions

• Host: GitHub
• URL: https://github.com/biaslab/iwai2024-ambiguity
• Owner: biaslab
• License: mit
• Created: 2022-11-21T21:32:34.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2024-09-03T20:27:12.000Z (almost 2 years ago)
• Last Synced: 2024-10-30T06:08:36.643Z (over 1 year ago)
• Topics: active-inference, bayesian-filtering, nonlinearity
• Language: Jupyter Notebook
• Homepage:
• Size: 92.4 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# IWAI2024-ambiguity

This is the companion repository to a paper presented at the International Workshop on Active Inference 2024 entitled:

"Planning to avoid ambiguous states through Gaussian approximations to nonlinear sensors in active inference agents."

The `demonstrations` folder contains notebooks with simple demonstrations of the model-predictive controller versus the free energy minimizing agents, as well as visualizations of the ambiguity landscape. The `experiments` folder details the Monte Carlo runs of the various free energy minimizing agents and the resulting visualizations.

## Feedback

Questions and comments can be addressed to the issues tracker.