AbdelAli Ed-Dbali

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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 of(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)
We present a way of integrating machine learning capabilities in constraint reasoning systems by the use of partially defined constraints called Open Constraints. This enables a form of constraint reasoning with incomplete information: we use a machine learning algorithm to guess the missing part of the constraint and we put immediately this knowledge into(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)
In this paper, we introduce a formal framework to describe CSP approximations (usually called consistencies), showing the importance of the language (or datastructure) used to perform this consistency. We introduce the notion of R-consistency which takes into account the representation of the data and which generalizes many known consistencies. We then(More)