Learning logic programs by discovering where not to search

• URL: http://arxiv.org/abs/2202.09806v1
• Date: Sun, 20 Feb 2022 12:32:03 GMT
• Title: Learning logic programs by discovering where not to search
• Authors: Andrew Cropper and C\'eline Hocquette
• Abstract summary: 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. Our experiments on multiple domains show that our approach can substantially reduce learning times.
• Score: 18.27510863075184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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