Inductive logic programming at 30

TL;DR

This paper reviews 30 years of inductive logic programming research, highlighting advances in meta-level search, recursive program learning, predicate invention, and technological integration, while discussing current limitations and future directions.

Contribution

It provides a comprehensive overview of recent developments in ILP, emphasizing novel methods and approaches introduced in the last decade.

Findings

Introduction of new meta-level search techniques

Advances in learning recursive programs

Innovations in predicate invention methods

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.