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arXiv:2102.10556cs.AI

Inductive logic programming at 30

Andrew Cropper, Sebastijan Dumančić, Richard Evans, Stephen H. Muggleton

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Abstract

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. 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.

arXiv comments
Extension of IJCAI20 survey paper. Accepted for the MLJ. arXiv admin note: substantial text overlap with arXiv:2002.11002, arXiv:2008.07912
Journal reference
DOI
Subjects
cs.AIArtificial Intelligence cs.LGMachine Learning

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Linked by 1 identifiable commenter in 2 comments across 2 threads, from Mar 2022 to Oct 2024. Found in 1 HN submission from Jul 2023 to Jul 2023. Open submission discussions.

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Discussed in comments2 threads

  1. Use Prolog to improve LLM's reasoning 1 comment

    • Oh, I didn't remember that Bratko had a chapter on ILP. I've met him in a couple of ILP conferences so I knew he's published work in ILP though [1]. The techniques described in the book are quite a bit older and, to be honest, they were rather limited, in particular with respect…

      YeGoblynQueenneView on HNParent context

  2. Neurosymbolic Programming (2021) [pdf] 1 comment

    • I study Inductive Logic Programming (ILP) which is a kind of inductive program synthesis. Inductive program synthesis is learning from partial specifications, where deductive program synthesis is learning from complete specifications (compilation can be seen as a form of…

      YeGoblynQueenneView on HNParent context

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