  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%.
