Andrei Legtchenko

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In this paper, we present an abstract framework for learning a finite domain constraint solver mod-eled 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(More)
What makes a good consistency ? Depending on the constraint, it may be a good pruning power or a low computational cost. By " weakening " arc-consistency, we propose to define new automatically generated solvers which form a sequence of consistencies weaker than arc-consistency. The method presented in this paper exploits a form of regularity in the cloud(More)
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(More)