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Communication Dans Un Congrès Année : 2003

Finite Domain Constraint Solver Learning

Résumé

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.
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Dates et versions

hal-00144947 , version 1 (12-03-2010)

Identifiants

  • HAL Id : hal-00144947 , version 1

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Arnaud Lallouet, Thi-Bich-Hanh Dao, Andrei Legtchenko, Abdelali Ed-Dbali. Finite Domain Constraint Solver Learning. Eighteenth International Joint Conference on Artificial Intelligence IJCAI-03, 2003, Acapulco, Mexico. pp.1379-1380. ⟨hal-00144947⟩
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