The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training examples and background knowledge (BK). To improve performance, we introduce an approach that, before searching for a hypothesis, first discovers `where not to search'. We use given BK to discover constraints on hypotheses, such as that a number cannot be both even and odd. We use the constraints to bootstrap a constraint-driven ILP system. Our experiments on multiple domains (including program synthesis and inductive general game playing) show that our approach can substantially reduce learning times.

翻译：感性逻辑编程(ILP)的目标是寻找一种假设,即概括培训实例和背景知识(BK)。为了改进绩效,我们引入了一种方法,在寻找假设之前,首先发现`哪里没有搜索'。我们利用给BK来发现对假设的限制,例如一个数字不可能是偶数和奇数。我们利用这些限制来陷阱受约束的ILP系统。我们在多个领域的实验(包括程序合成和感性一般游戏)表明,我们的方法可以大大缩短学习时间。