Inductive Logic Programming at 30
A New Introduction
More Info
expand_more
expand_more
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.
Files
Sminton_13507_Article_PDF_3088... (pdf)
(pdf | 0.71 Mb)
License info not available
Download not available