Learning big logical rules by joining small rules
This work addresses a major bottleneck in inductive logic programming for domains like game playing and drug design, representing an incremental advancement.
The paper tackles the challenge of learning large logical rules in inductive logic programming by introducing an approach that joins small rules to form big ones, achieving the ability to learn rules with over 100 literals and significantly outperforming existing methods in predictive accuracy.
A major challenge in inductive logic programming is learning big rules. To address this challenge, we introduce an approach where we join small rules to learn big rules. We implement our approach in a constraint-driven system and use constraint solvers to efficiently join rules. Our experiments on many domains, including game playing and drug design, show that our approach can (i) learn rules with more than 100 literals, and (ii) drastically outperform existing approaches in terms of predictive accuracies.