Inductive Logic Programming At 30: A New Introduction

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 setti...

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Veröffentlicht in:The Journal of artificial intelligence research Jg. 74; S. 765 - 850
Hauptverfasser: Cropper, Andrew, Dumančić, Sebastijan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: San Francisco AI Access Foundation 01.01.2022
Schlagworte:
Artificial intelligence
Logic programming
Machine learning
ISSN:1076-9757, 1076-9757, 1943-5037
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1076-9757
1076-9757
1943-5037
DOI:10.1613/jair.1.13507