Proceedings

Finite Domain Constraint Solver Learning

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Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 1379-1379.

Abstract

In this paper, we present an abstract framework for learning a finite domain constraint solver modeled by a set of operators enforcing a consistency. The behavior of the consistency to be learned is taken as the set of examples on which the learning process is applied. The best possible expression of this operator in a given language is then searched. We instantiate this framework to the learning of bound-consistency in the indexical language of Gnu-Prolog.

Arnaud Lallouet, Thi-Bich-Hanh Dao, Andrei Legtchenko, and AbdelAli Ed-Dbali. 2003. Finite Domain Constraint Solver Learning. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03. IJCAI, 1379–1379. https://ijcai.org/proceedings/2003/205
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