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%.

翻译：归纳逻辑编程（ILP）的目标是搜索能够泛化训练示例和背景知识的逻辑程序。我们提出了一种识别最小不可满足子程序（MUSP）的ILP方法。研究表明，寻找MUSP能够高效且可靠地剪枝搜索空间。在包括程序合成和游戏对战在内的多个领域实验中，我们的方法可将学习时间减少99%。