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)
Partially defined constraints can be used to model the incomplete knowledge of a concept or a relation. Instead of only computing with the known part of the constraint, we propose to complete its definition by using machine learning techniques. Since constraints are actively used during solving for pruning domains, building a classifier for instances is not(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)