Inductive logic programming at 30

• URL: http://arxiv.org/abs/2102.10556v1
• Date: Sun, 21 Feb 2021 08:37:17 GMT
• Title: Inductive logic programming at 30
• Authors: Andrew Cropper, Sebastijan Duman\v{c}i\'c, Richard Evans, and Stephen H. Muggleton
• Abstract summary: Inductive logic programming (ILP) is a form of logic-based machine learning. We focus on (i) new meta-level search methods, (ii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing some of the current limitations of ILP and discuss directions for future research.
• Score: 22.482292439881192
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inductive logic programming (ILP) is a form of logic-based machine learning. The goal of ILP is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we survey recent work in the field. In this survey, we focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs that generalise from few examples, (iii) new approaches for predicate invention, and (iv) the use of different technologies, notably answer set programming and neural networks. We conclude by discussing some of the current limitations of ILP and discuss directions for future research.

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