Learning MDL Logic Programs from Noisy Data
DOI:
https://doi.org/10.1609/aaai.v38i9.28925Keywords:
KRR: Logic Programming, ML: Statistical Relational/Logic LearningAbstract
Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including recursive programs. Our experiments on several domains, including drug design, game playing, and program synthesis, show that our approach can outperform existing approaches in terms of predictive accuracies and scale to moderate amounts of noise.Downloads
Published
2024-03-24
How to Cite
Hocquette, C., Niskanen, A., Järvisalo, M., & Cropper, A. (2024). Learning MDL Logic Programs from Noisy Data. Proceedings of the AAAI Conference on Artificial Intelligence, 38(9), 10553-10561. https://doi.org/10.1609/aaai.v38i9.28925
Issue
Section
AAAI Technical Track on Knowledge Representation and Reasoning