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<div align="center">
<h1> Neural Reasoning about Agents' Goals, Preferences, and Actions </h1>
**[Matteo Bortoletto][1], &nbsp; [Lei Shi][2], &nbsp; [Andreas Bulling][3]** <br> <br>
**AAAI'24, Vancouver, CA** <br>
# Citation
If you find our code useful or use it in your own projects, please cite our paper:
title={Neural Reasoning About Agents Goals, Preferences, and Actions},
author={Bortoletto, Matteo and Shi, Lei and Bulling, Andreas},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
# Setup
This code is based on the [original implementation][5] 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.
## Training
## Testing
# Hardware setup
All models are trained on an NVIDIA Tesla V100-SXM2-32GB GPU.