Turning 30: New Ideas in Inductive Logic Programming

• URL: http://arxiv.org/abs/2002.11002v4
• Date: Wed, 22 Apr 2020 09:06:19 GMT
• Title: Turning 30: New Ideas in Inductive Logic Programming
• Authors: Andrew Cropper, Sebastijan Duman\v{c}i\'c, and Stephen H. Muggleton
• Abstract summary: inductive logic programming is a form of machine learning that induces logic programs from data. We focus on new methods for learning programs that generalise from few examples. We also discuss directions for future research in inductive logic programming.
• Score: 18.581514902689346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data. We survey recent work in inductive logic programming (ILP), a form of machine learning that induces logic programs from data, which has shown promise at addressing these limitations. We focus on new methods for learning recursive programs that generalise from few examples, a shift from using hand-crafted background knowledge to \emph{learning} background knowledge, and the use of different technologies, notably answer set programming and neural networks. As ILP approaches 30, we also discuss directions for future research.

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