https://github.com/silviatulli/suboptimax

minmax planner for suboptimal explanations - Explainable Agency by Revealing Suboptimality in CHRI Learning Scenarios
https://github.com/silviatulli/suboptimax

catkin chri-learning-scenarios explainable-ai minmax-algorithm pynaoqi-sdk ros-melodic sequential-decision-making-problems

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minmax planner for suboptimal explanations - Explainable Agency by Revealing Suboptimality in CHRI Learning Scenarios

• Host: GitHub
• URL: https://github.com/silviatulli/suboptimax
• Owner: Silviatulli
• Created: 2020-10-23T12:37:12.000Z (about 5 years ago)
• Default Branch: main
• Last Pushed: 2021-06-02T16:03:46.000Z (over 4 years ago)
• Last Synced: 2025-05-01T12:05:21.911Z (7 months ago)
• Topics: catkin, chri-learning-scenarios, explainable-ai, minmax-algorithm, pynaoqi-sdk, ros-melodic, sequential-decision-making-problems
• Language: Python
• Homepage:
• Size: 33.2 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
## Explainable Agency by Revealing Suboptimalityin Child-Robot Learning Scenarios

Revealing the internal workings of a robot can help a human better understand the robot's behaviors. How to reveal such workings, e.g., via explanation generation, remains a significant challenge. This gets even more complex when these explanations are targeted towards children.

Therefore, we propose a search-based approach to generate contrastive explanations using optimal and sub-optimal plans and implement it in a scenario for children. In the application scenario, the child and the robot learn together how to play a zero-sum game that requires logical and mathematical thinking.

We report results around our explanation generation system that was successfully deployed among seven-year-old children. Our results show trends that the generated explanations were able to positively affect the children's perceived difficulty in learning the zero-sum game.

For additional details about the research work you can check out our paper: [Explainable Agency by Revealing Suboptimalityin Child-Robot Learning Scenarios](https://link.springer.com/chapter/10.1007/978-3-030-62056-1_3)

## Test

To run the code:

- open a terminal and launch: $ roscore

- open a second terminal into your repository folder and launch minmax.launch: $ roslaunch minmax.launch

## Config

- **Install [ROS melodic](http://wiki.ros.org/melodic) and [catkin](https://wiki.ros.org/catkin#Installing_catkin)**

- **Clone the repository**

   - $ git clone https://github.com/Silviatulli/suboptimax.git

- **Make the python files executable** 

 Run the following command for each script:

 - $ chmod +x filename.py

- **Build a catkin workspace and source the setup file**

  - $ cd ~/catkin_ws

  - $ catkin_make

- **Add the workspace to the ROS environment**

  - $. ~/catkin_ws/devel/setup.bash

- **Make sure that the CMakeLists.txt file is configured properly** 

   - All the services and the dependencies should be as follows:

      - find_package (catkin REQUIRED COMPONENTS roscpp rospy std_msgs message_generation message_runtime )

      - add_service_files (FILES Decision.srv GameState.srv Plan.srv RobotExplanation.srv RobotTalk.srv )

- **Make it work with the Robot**

   - If you work with the NAO Robot uncomment the line 7, from 41 to 65 and 85 (self.robot_communication.say(self.explanation_text)) in the file robot_manager.py. 

   - Create a folder sdk that contains the pynaoqi sdk required and modify your bashrc ($gedit ~/.bashrc) to indicate the python and library paths as follows:

      - export PYTHONPATH=$PYTHONPATH:~/sdk/pynaoqi-python2.7-2.1.2.17-linux64

      - export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/home//sdk/pynaoqi-python2.7-2.1.2.17-linux64

      - you can download the pynaoqi sdk following this [guide](http://wiki.ros.org/nao/Tutorials/Installation)