1099 - Symmetry Breaking for Inductive Logic Programming

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Authors: Association for Artificial Intelligence 2026, David M. Cerna, Andrew Cropper, Matti J„ärvisalo
Cited by: 0

Abstract

The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training data and background knowledge. The challenge is searching vast hypothesis spaces, which is exacerbated because many logically equivalent hypotheses exist. To address this challenge, we introduce a method to break symmetries in the hypothesis space. We implement our idea in answer set programming. Our experiments on multiple domains, including visual reasoning and game playing, show that our approach can reduce solving times from over an hour to just 17 seconds.

Fields of Study & Subjects

Logic Reasoning and Knowledge AI-based Problem Solving and Planning Data Mining Algorithms and Applications

Publication details

Journal / source
Underline Science Inc.
Publication date
7 January 2026
Language
EN