Finite domain constraint solver learning

Authors:
Arnaud Lallouet;Thi-Bich-Hanh Dao;Andrei Legtchenko;AbdelAli Ed-Dbali
Affiliations:
University d'Orleans, LIFO, Orleans, France;University d'Orleans, LIFO, Orleans, France;University d'Orleans, LIFO, Orleans, France;University d'Orleans, LIFO, Orleans, France
Venue:
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Year:
2003

Citing 4
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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.