Towards Learning Abductive Reasoning using VSA Distributed Representations

Giacomo Camposampiero, Michael Hersche, Aleksandar Terzić, Roger Wattenhofer, Abu Sebastian and Abbas Rahimi [Paper]

Accepted as Conference Paper (Spotlight) at the 18th International Conference on Neural-Symbolic Learning and Reasoning (NeSy), 2024.

Build the Environment 🛠️

Hardware

You will need a machine with a CUDA-enabled GPU and the Nvidia SDK installed to compile the CUDA kernels. We tested our methods on an NVIDA Tesla V100 GPU with CUDA Version 11.3.1.

Installing Dependencies

The mamba software is required for running the code. You can create a new mamba environment using

mamba create --name arlc python=3.7
mamba activate arlc

To install PyTorch 1.11 and CUDA, use

mamba install pytorch==1.11.0 torchvision==0.12.0 cudatoolkit=11.3 -c pytorch -c conda-forge

Clone and install the neuro-vsa repo (some of their utils are re-used in this project)

git clone https://github.com/IBM/neuro-vector-symbolic-architectures.git
cd neuro-vector-symbolic-architectures
pip install -e . --no-dependencies

Finally, clone and install this repo

git clone https://github.com/IBM/abductive-rule-learner-with-context-awareness.git
cd abductive-rule-learner-with-context-awareness
pip install -r requirements.txt
pip install -e .
pre-commit install

We suggest to format the code of the entire repository to improve its readability. To do so, please install and run black

pip install black
black abductive-rule-learner-with-context-awareness/

I-RAVEN Dataset

You can find the instructions to download and pre-process the data in the data folder.

Run our Experiments 🔬

You can replicate the main experiments shown in the paper with the following scripts

# ARLC learned from data
./experiments/arlc_learn.sh
# ARLC initialized with programming, then learned
./experiments/arlc_progr_to_learn.sh
# ARLC programmed and evaluated
./experiments/arlc_progr.sh

To replicate our ablations on the introduced contributions, run

# line 1 ablation table
# obtained with the code from https://github.com/IBM/learn-vector-symbolic-architectures-rule-formulations, modified to run with multiple random seeds

# line 2 ablation table
./experiments/ablations/learnvrf_nopn_2x2.sh

# line 3 ablation table
./experiments/ablations/context.sh

# line 4 ablation table
# same as ./experiments/arlc_learn.sh

To replicate our OOD experiments, run

./experiments/arlc_ood.sh

Citation 📚

If you use the work released here for your research, please consider citing our paper:

@inproceedings{camposampiero2024towards,
  title={Towards Learning Abductive Reasoning using VSA Distributed Representations},
  author={Camposampiero, Giacomo and Hersche, Michael and Terzi{\'c}, Aleksandar and Wattenhofer, Roger and Sebastian, Abu and Rahimi, Abbas},
  booktitle={18th International Conference on Neural-Symbolic Learning and Reasoning (NeSy)},
  year={2024},
  month={sep}
}

License 🔏

Please refer to the LICENSE file for the licensing of our code. Our implementation relies on PrAE released under GPL v3.0 and Learn-VRF released under GPL v3.0.

IBM / abductive-rule-learner-with-context-awareness

readme