Learning logic programs by finding minimal unsatisfiable subprograms

Andrew Cropper, Céline Hocquette
1/29/2024
cs.LGcs.LO

No confident code implementation found for this paper.

Abstract

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%.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Honey, I shrunk the hypothesis space (through logical preprocessing)

84% match2025

Self-Supervised Inductive Logic Programming

83% match2025

An Empirical Comparison of Cost Functions in Inductive Logic Programming

82% match2025

Symbolic Snapshot Ensembles

82% match2025

Learning Logical Rules using Minimum Message Length

82% match2025

Learning MDL logic programs from noisy data

82% match1 repo72023

Cite this paper

@article{cropper2024learning,
  title  = {Learning logic programs by finding minimal unsatisfiable subprograms},
  author = {Andrew Cropper and Céline Hocquette},
  year   = {2024},
  eprint = {2401.16383},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.16383v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2401.16383.svg)](https://codeofpaper.com/paper/2401.16383)

Discussion