Inductive logic programming at 30: a new introduction

• URL: http://arxiv.org/abs/2008.07912v5
• Date: Tue, 22 Mar 2022 10:44:16 GMT
• Title: Inductive logic programming at 30: a new introduction
• Authors: Andrew Cropper and Sebastijan Duman\v{c}i\'c
• Abstract summary: Inductive logic programming (ILP) is a form of machine learning. This paper introduces the necessary logical notation and the main learning settings. We also describe the building blocks of an ILP system and compare several systems.
• Score: 18.27510863075184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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