Inductive logic programming at 30: a new introduction
Artificial Intelligence
2022-03-23 v5 Machine Learning
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.
Cite
@article{arxiv.2008.07912,
title = {Inductive logic programming at 30: a new introduction},
author = {Andrew Cropper and Sebastijan Dumančić},
journal= {arXiv preprint arXiv:2008.07912},
year = {2022}
}
Comments
Preprint of a paper accepted for JAIR