2.1 KiB

Neural Reasoning about Agents' Goals, Preferences, and Actions

Matteo Bortoletto,   Lei Shi,   Andreas Bulling

AAAI'24, Vancouver, CA


If you find our code useful or use it in your own projects, please cite our paper:

  author = {Bortoletto, Matteo and Lei, Shi and Bulling, Andreas},
  title = {{Neural Reasoning about Agents' Goals, Preferences, and Actions}},
  booktitle = {Proc. 38th AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},


This code is based on the original implementation of the BIB benchmark.

Using virtualenv

python -m virtualenv /path/to/env
source /path/to/env/bin/activate
pip install -r requirements.txt

Using conda

conda create --name <env_name> python=3.8.10 pip=20.0.2 cudatoolkit=10.2.89
conda activate <env_name>
pip install -r requirements_conda.txt
pip install dgl-cu102 dglgo -f

Running the code

Activate the environment

Run source bibdgl/bin/activate.

Index data

This will create the json files with all the indexed frames for each episode in each video.

python utils/

You need to manually set mode in the dataset class (in main).

Generate graphs

This will generate the graphs from the videos:

python /utils/ --mode MODE --cpus NUM_CPUS

MODE can be train, val or test. NOTE: check utils/ to make sure you're loading the correct dataset to generate the graphs you want.





Hardware setup

All models are trained on an NVIDIA Tesla V100-SXM2-32GB GPU.