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Learning constraint satisfaction problems: An ILP perspective
Department of Computer Science, KU Leuven, Leuven, Belgium.ORCID iD: 0000-0002-6860-6303
Department of Computer Science, KU Leuven, Leuven, Belgium.
Department of Computer Science, KU Leuven, Leuven, Belgium.
CNRS, University of Montpellier, Montpellier, France.
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We investigate the problem of learning constraint satisfaction problems from an inductive logic programming perspective. Constraint satisfaction problems are the underlying basis for constraint programming and there is a long standing interest in techniques for learning these. Constraint satisfaction problems are often described using a relational logic, so inductive logic programming is a natural candidate for learning such problems. So far, there is however only little work on the intersection between learning constraint satisfaction problems and inductive logic programming. In this note, we point out several similarities and differences between the two classes of techniques and use these to propose several interesting research challenges.

Place, publisher, year, edition, pages
2014. Vol. 24, p. 1-6
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:oru:diva-91996OAI: oai:DiVA.org:oru-91996DiVA, id: diva2:1558075
Conference
24th International Conference on Inductive Logic Programming (ILP 2014), Nancy, France, September 14-16, 2014
Note

Funding Agency:

European Commission under the project Inductive Constraint Programming (FP7- 284715)

Available from: 2021-05-28 Created: 2021-05-28 Last updated: 2024-01-16Bibliographically approved

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De Raedt, Luc

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