Inductive logic programming at 30: a new introduction

Andrew Cropper; Sebastijan Duman\v{c}i\'c

Inductive logic programming at 30: a new introduction

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arxiv 2008.07912 v5 pith:MCAJAHDC submitted 2020-08-18 cs.AI cs.LG

Andrew Cropper , Sebastijan Dumančić This is my paper · ORCID

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Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main learning settings; describe the building blocks of an ILP system; compare several systems on several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol); highlight key application areas; and, finally, summarise current limitations and directions for future research.

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Bridging Logic Programming and Deep Learning for Explainability through ILASP
cs.LO 2025-02 unverdicted novelty 3.0 of 10

A research plan proposes pairing neural networks with ILP systems so that AI predictions come with human-readable logical rules, with early tests in weather, law, and biology.

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