Conference item
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 invented predicates. Our experimental results show that types can substantially reduce learning times.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
-
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(Preview, Accepted manuscript, pdf, 183.4KB, Terms of use)
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- Publisher copy:
- 10.1007/978-3-030-19570-0_13
Authors
- Publisher:
- Department of Mathemathics and Computer Science, University of Calabria
- Host title:
- European Conference on Logics in Artificial Intelligence
- Journal:
- European Conference on Logics in Artificial Intelligence More from this journal
- Volume:
- 11468
- Pages:
- 198-213
- Series:
- Lecture Notes in Computer Science
- Publication date:
- 2019-05-06
- Acceptance date:
- 2019-01-18
- DOI:
- ISSN:
-
0302-9743
- Pubs id:
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pubs:971007
- UUID:
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uuid:0831071e-1986-4ffe-a45c-028f55a18530
- Local pid:
-
pubs:971007
- Source identifiers:
-
971007
- Deposit date:
-
2019-02-12
Terms of use
- Copyright holder:
- Springer
- Copyright date:
- 2019
- Notes:
- © Springer Nature Switzerland AG 2019. This is the accepted manuscript version of the article. The final version is available online from Springer at: https://doi.org/10.1007/978-3-030-19570-0_13. This paper has been accepted for presentation at the 16th European Conference on Logics in Artificial Intelligence, 07-11 May 2019, Rende Italy
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