Inductive logic programming at 30
Artificial Intelligence
2021-09-23 v2 Machine Learning
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. 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.
Cite
@article{arxiv.2102.10556,
title = {Inductive logic programming at 30},
author = {Andrew Cropper and Sebastijan Dumančić and Richard Evans and Stephen H. Muggleton},
journal= {arXiv preprint arXiv:2102.10556},
year = {2021}
}
Comments
Extension of IJCAI20 survey paper. Accepted for the MLJ. arXiv admin note: substantial text overlap with arXiv:2002.11002, arXiv:2008.07912