A collection of neural DNF-based models based on semi-symbolic layers introduced in the pix2rule [1].
The following models are implemented in neural_ndnf/neural_dnf.py:
Neural DNF (NeuralDNF): The vanilla neural DNF model from pix2rule [1],
consisting of a conjunctive semi-symbolic layer and a disjunctive neural layer,
both with tanh activation.
Neural DNF-EO (NeuralDNFEO): An extension of the neural DNF model from
[2], with a frozen conjunctive layer as a constraint layer after the disjunctive
layer.
Neural DNF-MT (NeuralDNFMutexTanh): Our new model introduced in the paper
[3], consisting of a conjunctive semi-symbolic layer with tanh activation and a
disjunctive neural layer with the mutex-tanh activation (implemented as a
function mutex_tanh(...) in neural_ndnf/common.py). The semi-symbolic layer
with mutex-tanh activation is implemented as class SemiSymbolicMutexTanh in
neural_ndnf/semi_symbolic.py .
Install this package in editable mode:
pip install -e . --config-settings editable_mode=strictThe config setting flag is used for resolving Pylance's issue with importing.
To run the unit test, run the following command in the root directory of the project:
python -m unittest discover -p "*_test.py"[1] Cingillioglu, N., & Russo, A. (2021). pix2rule: End-to-end Neuro-symbolic Rule Learning. In A. S. D. Garcez & E. Jiménez-Ruiz (Eds.), Proceedings of the 15th International Workshop on Neural-Symbolic Learning and Reasoning as part of the 1st International Joint Conference on Learning & Reasoning (IJCLR 2021), Virtual conference, October 25-27, 2021 (pp. 15–56). Retrieved from https://ceur-ws.org/Vol-2986/paper3.pdf
[2] Baugh, K. G., Cingillioglu, N., & Russo, A. (2023). Neuro-symbolic Rule Learning in Real-world Classification Tasks. In A. Martin, H.-G. Fill, A. Gerber, K. Hinkelmann, D. Lenat, R. Stolle, & F. van Harmelen (Eds.), Proceedings of the AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering (AAAI-MAKE 2023), Hyatt Regency, San Francisco Airport, California, USA, March 27-29, 2023. Retrieved from https://ceur-ws.org/Vol-3433/paper12.pdf
[3] Kexin Gu Baugh, Luke Dickens, and Alessandra Russo. 2025. Neural DNF-MT: A Neuro-symbolic Approach for Learning Interpretable and Editable Policies. In Proc. of the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025), Detroit, Michigan, USA, May 19 – 23, 2025, IFAAMAS. Arxiv pre-print: https://arxiv.org/abs/2501.03888