Learning MDL Logic Programs from Noisy Data

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.

Authors

• Céline Hocquette University of Oxford
• Andreas Niskanen University of Helsinki
• Matti Järvisalo University of Helsinki
• Andrew Cropper University of Oxford

Keywords

• KRR: Logic Programming
• ML: Statistical Relational/Logic Learning