Inductive logic programming at 30

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,...

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Bibliographic Details
Published in:Machine learning Vol. 111; no. 1; pp. 147 - 172
Main Authors: Cropper, Andrew, Dumančić, Sebastijan, Evans, Richard, Muggleton, Stephen H.
Format: Journal Article
Language:English
Published: New York Springer US 01.01.2022
Springer Nature B.V
Subjects:
Artificial Intelligence
Computer Science
Control
Logic programming
Logic programs
Machine Learning
Mechatronics
Natural Language Processing (NLP)
Robotics
S.i.: Lr 2020
Simulation and Modeling
Special issue on Learning and Reasoning
Relational learning
Inductive logic programming
Program synthesis
Program induction
ISSN:0885-6125, 1573-0565
Online Access:Get full text
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Summary: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.
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ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-021-06089-1