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Agent Interrogation Algorithm (AIA)

This repository contains the code for the paper:

Asking the Right Questions: Learning Interpretable Action Models Through Query Answering.
Pulkit Verma, Shashank Rao Marpally, and Siddharth Srivastava.
35th AAAI Conference on Artificial Intelligence, 2021.

Paper | Extended Version | Talk | Slides | Poster

Directory Structure

|-- dependencies/
|   |-- FD/
|   |-- FF/
|   |-- pddlgym/
|   |-- VAL/
|-- domains/
|-- random_states/
|-- results/
|-- src/
|   |-- agent.py
|   |-- config.py
|   |-- generate_random_states.py
|   |-- main.py
|   |-- interrogation/
|   |-- lattice/
|   |-- query/
|   |-- sim/
|   |-- utils/
|-- README.md
|-- LICENSE
  • dependencies: This directory includes the external software used to run the code. This includes FF, FD, VAL, and PDDLGym.

  • dependencies: Place all the domains in this directory. There must be a directory for each domain containing:

    • domain.pddl (domain file for that domain), and
    • instances directory containing the problem files for that domain.
  • random_states: This directory stores the set of states in serialized form. For each domain, there is a .pkl file containing 60 states approximately. These are generated using src/generate_random_states.py.

  • src: This directory stores the source code for AIA. It contains 4 files:

    • agent.py: Contains the agent code.
    • config.py: Declares the configuration parameters.
    • generate_random_states.py: Generates the random states for each domain.
    • main.py : Contains the main driver code which runs the code end-to-end.

    src also contains code structured into following sub-directories:

    • interrogation: Contains the AIA code.
    • lattice: Contains the model and lattice classes.
    • query: Contains the plan outcome query code.
    • sim: Simulator specific code. Contains a separate agent file for each simulator domain.
    • utils: Contains general utilities.
      • utils/parser: Code based on PDDLGym.
      • utils/translate: Code based on FD.

Configuration

Configuration parameters are set in src/config.py

  • FF_PATH, FD_PATH, and VAL_PATH stores the relative path of FF, FD, and VAL respectively.
  • NUM_PER_DOMAIN denotes how many instances per domain must be run. Keep minimum 2 for symbolic agent.
  • PLANNER specifies which planner to use. Set it to either FF or FD.
  • Comment out either Symbolic Agent Settings or Simulator Agent Settings.

How to Run

  1. Install the required software
pip3 install -r requirements.txt 
  1. Adjust variables/paramters as needed in src/config.py.

  2. Run main.py

cd src
python3 main.py

Common Installation Issues

  1. OpenCV (Tested on Ubuntu 18.04)

    Refer to https://linuxize.com/post/how-to-install-opencv-on-ubuntu-18-04/

  2. FF:

    • Please install flex and bison for FF to compile.

    • On newer versions of gcc (tested on gcc 10.2.0) please make the following changes:

      • main.c:150 : Comment out the gbracket_count definition
        int gbracket_count; --> /* int gbracket_count; */
        
      • relax.c:111 : Define lcurrent_goals as static
        State lcurrent_goals; --> static State lcurrent_goals;
        
      • search.c:110 : Define lcurrent_goals as static
        State lcurrent_goals; --> static State lcurrent_goals;
        

Please note that this is research code and not yet ready for public delivery, hence most parts are not documented.

In case of any queries, please contact verma.pulkit@asu.edu, or smarpall@asu.edu.

Contributors

Pulkit Verma
Shashank Rao Marpally
Abhyudaya Srinet
Siddharth Srivastava


Citation

@inproceedings{verma_2021_asking,
    author = {Verma, Pulkit and Marpally, Shashank Rao and Srivastava, Siddharth},
    title = {{Asking the Right Questions: Learning Interpretable Action Models Through Query Answering}},
    booktitle = {Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)},
    year={2021}
}

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