Neural Reasoning about Agents' Goals, Preferences, and Actions
**[Matteo Bortoletto][1], [Lei Shi][2], [Andreas Bulling][3]**
**AAAI'24, Vancouver, CA**
**[[Paper][4]]**
# Citation
If you find our code useful or use it in your own projects, please cite our paper:
```bibtex
@inproceedings{bortoletto2024neural,
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},
}
```
# Setup
This code is based on the [original implementation][5] of the BIB benchmark.
## Using `virtualenv`
```bash
python -m virtualenv /path/to/env
source /path/to/env/bin/activate
pip install -r requirements.txt
```
## Using `conda`
```bash
conda create --name python=3.8.10 pip=20.0.2 cudatoolkit=10.2.89
conda activate
pip install -r requirements_conda.txt
pip install dgl-cu102 dglgo -f https://data.dgl.ai/wheels/repo.html
```
# 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.
```bash
python utils/index_data.py
```
You need to manually set `mode` in the dataset class (in main).
## Generate graphs
This will generate the graphs from the videos:
```bash
python /utils/build_graphs.py --mode MODE --cpus NUM_CPUS
```
`MODE` can be `train`, `val` or `test`. NOTE: check `utils/build_graphs.py` to make sure you're loading the correct dataset to generate the graphs you want.
## Training
Use `CUDA_VISIBLE_DEVICES=0 run_train.sh`.
## Testing
Use `CUDA_VISIBLE_DEVICES=0 run_test.sh`.
# Hardware setup
All models are trained on an NVIDIA Tesla V100-SXM2-32GB GPU.
[1]: https://mattbortoletto.github.io/
[2]: https://perceptualui.org/people/shi/
[3]: https://perceptualui.org/people/bulling/
[4]: https://perceptualui.org/publications/bortoletto24_aaai.pdf
[5]: https://github.com/kanishkg/bib-baselines