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#1 Learning Logic Programs by Discovering Where Not to Search [PDF1] [Copy] [Kimi] [REL]

Authors: Andrew Cropper, Céline Hocquette

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 (i) substantially reduce learning times by up to 97%, and (ii) can scale to domains with millions of facts.

Subject: AAAI.2023 - Knowledge Representation and Reasoning