Inductive logic programming at 30

doi:10.1007/s10994-021-06089-1

Inductive logic programming at 30

2021-11-09

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DOI：10.1007/s10994-021-06089-1

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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.

Keywords:

Inductive logic programming

Relational learning

Program synthesis

Program induction

Inductive logic programming at 30

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