Inductive Logic Programming at 30: A New Introduction
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
Text
Cropper and Dumancic. "Inductive Logic Programming at 30: A New Introduction." Journal of Artificial Intelligence Research, 2022. doi:10.1613/JAIR.1.13507Markdown
[Cropper and Dumancic. "Inductive Logic Programming at 30: A New Introduction." Journal of Artificial Intelligence Research, 2022.](https://mlanthology.org/jair/2022/cropper2022jair-inductive/) doi:10.1613/JAIR.1.13507BibTeX
@article{cropper2022jair-inductive,
title = {{Inductive Logic Programming at 30: A New Introduction}},
author = {Cropper, Andrew and Dumancic, Sebastijan},
journal = {Journal of Artificial Intelligence Research},
year = {2022},
pages = {765-850},
doi = {10.1613/JAIR.1.13507},
volume = {74},
url = {https://mlanthology.org/jair/2022/cropper2022jair-inductive/}
}