Inductive logic programming at 30

Journal article (2021)

Authors

Andrew Cropper University of Oxford

S. Dumančić Katholieke Universiteit Leuven

Richard Evans Imperial College London

Stephen H. Muggleton Imperial College London

Affiliation

External organisation

Relational learning Program synthesis Inductive logic programming Program induction

To reference this document use:

http://resolver.tudelft.nl/uuid:0d81b96b-393b-4d09-af63-cef543b6b0ca

More Info

expand_more

Published Date

2021

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Affiliation

External organisation

Abstract

Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing current limitations of ILP and directions for future research.