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Learning higher-order logic programs

Abstract:

A key feature of inductive logic programming is its ability to learn first-order programs, which are intrinsically more expressive than propositional programs. In this paper, we introduce techniques to learn higher-order programs. Specifically, we extend meta-interpretive learning (MIL) to support learning higher-order programs by allowing for higher-order definitions to be used as background knowledge. Our theoretical results show that learning higher-order programs, rather than first-order ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s10994-019-05862-7

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Oxford college:
St John's College
Role:
Author
Publisher:
Springer
Journal:
Machine Learning More from this journal
Volume:
109
Issue:
7
Pages:
1289–1322
Publication date:
2019-12-03
Acceptance date:
2019-11-14
DOI:
EISSN:
1573-0565
ISSN:
0885-6125
Language:
English
Keywords:
Pubs id:
pubs:1078147
UUID:
uuid:fb42efb4-1863-4cd6-8b1d-a8bf2be3c6ad
Local pid:
pubs:1078147
Source identifiers:
1078147
Deposit date:
2019-12-30

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