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Typed meta-interpretive learning of logic programs

Abstract:

Meta-interpretive learning (MIL) is a form of inductive logic programming that learns logic programs from background knowledge and examples. We claim that adding types to MIL can improve learning performance. We show that type checking can reduce the MIL hypothesis space by a cubic factor. We introduce two typed MIL systems: Metagol T and HEXMIL T , implemented in Prolog and Answer Set Programming (ASP), respectively. Both systems support polymorphic types and can infer the types of invent...

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Publication status:
Published
Peer review status:
Peer reviewed

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Files:
  • (Accepted manuscript, pdf, 183.4KB)
Publisher copy:
10.1007/978-3-030-19570-0_13

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Oxford college:
Merton College
Role:
Author
ORCID:
0000-0001-7509-680X
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Institution:
University of Oxford
Oxford college:
St John's College
Role:
Author
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Institution:
University of Oxford
Oxford college:
Hertford College
Role:
Author
Publisher:
Department of Mathemathics and Computer Science, University of Calabria Publisher's website
Journal:
European Conference on Logics in Artificial Intelligence Journal website
Volume:
11468
Pages:
198-213
Series:
Lecture Notes in Computer Science
Host title:
European Conference on Logics in Artificial Intelligence
Publication date:
2019-05-06
Acceptance date:
2019-01-18
DOI:
ISSN:
0302-9743
Source identifiers:
971007
Pubs id:
pubs:971007
UUID:
uuid:0831071e-1986-4ffe-a45c-028f55a18530
Local pid:
pubs:971007
Deposit date:
2019-02-12

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