Inductive logic programming at 30: a new introduction
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Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a logic program (a set of logical rules) that generalises training examples. As ILP approaches 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main ILP learning settings. We describe the main building blocks of an ILP system. We compare several ILP systems on several dimensions. We detail four systems (Aleph, TILDE, ASPAL, and Metagol). We contrast ILP with other forms of machine learning. Finally, we summarise the current limitations and outline promising directions for future research.
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