Inductive logic programming at 30

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 and background knowledge. 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.

Automated summary

Inductive Logic Programming (ILP) is a form of logic-based machine learning that focuses on inducing hypotheses that generalize given training examples and background knowledge. Recent research has highlighted new meta-level search methods, techniques for learning recursive programs, approaches for predicate invention, and the use of different technologies. Current limitations of ILP and directions for future research are also being discussed.