Learning logic programs through divide, constrain, and conquer
This addresses the challenge of efficiently learning optimal and recursive logic programs for applications like classification and game playing, though it appears incremental as it builds on existing search methods.
The authors tackled the problem of learning logic programs by introducing an approach that combines divide-and-conquer search with constraint-driven search, resulting in increased predictive accuracies and reduced learning times across classification, inductive general game playing, and program synthesis domains.
We introduce an inductive logic programming approach that combines classical divide-and-conquer search with modern constraint-driven search. Our anytime approach can learn optimal, recursive, and large programs and supports predicate invention. Our experiments on three domains (classification, inductive general game playing, and program synthesis) show that our approach can increase predictive accuracies and reduce learning times.