Learning big logical rules by joining small rules
arXiv preprint arXiv:2401.16215, 2024•arxiv.org
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.
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.
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.
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