Learning MDL logic programs from noisy data
This addresses the challenge of noisy data in inductive logic programming, which is incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of learning logic programs from noisy data by introducing a minimal description length approach, achieving higher predictive accuracies and scalability to moderate noise levels in domains like drug design and game playing.
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.