Inductive logic programming at 30: a new introduction
This is an incremental review paper aimed at researchers and practitioners in machine learning and logic programming, summarizing existing knowledge without introducing novel methods.
The paper provides a new introduction to inductive logic programming (ILP) as it reaches 30 years, covering its foundations, systems, applications, and future directions, but does not present new experimental results or concrete numbers.
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.