Learning logic programs by finding minimal unsatisfiable subprograms
This addresses efficiency challenges in inductive logic programming for researchers and practitioners, though it appears incremental as it builds on existing ILP methods.
The paper tackles the problem of inductive logic programming by introducing an approach that identifies minimal unsatisfiable subprograms to prune the search space, resulting in a 99% reduction in learning times across domains like program synthesis and game playing.
The goal of inductive logic programming (ILP) is to search for a logic program that generalises training examples and background knowledge. We introduce an ILP approach that identifies minimal unsatisfiable subprograms (MUSPs). We show that finding MUSPs allows us to efficiently and soundly prune the search space. Our experiments on multiple domains, including program synthesis and game playing, show that our approach can reduce learning times by 99%.