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Chapitre D'ouvrage Année : 2016

Adapting Consistency in Constraint Solving

Résumé

State-of-the-art constraint solvers uniformly maintain the same level of local consistency (usually arc consistency) on all the instances. We propose two approaches to adjust the level of consistency depending on the instance and on which part of the instance we propagate. The first approach, parameterized local consistency, uses as parameter the stability of values, which is a feature computed by arc consistency algorithms during their execution. Parameterized local consistencies choose to enforce arc consistency or a higher level of local consistency to a value depending on whether the stability of the value is above or below a given threshold. In the adaptive version, the parameter is dynamically adapted during search, and so is the level of local consistency. In the second approach, we focus on partition-one-AC, a singleton-based consistency. We propose adaptive variants of partition-one-AC that do not necessarily run until having proved the fixpoint. The pruning can be weaker than the full version, but the computational effort can be significantly reduced. Our experiments show that adaptive parameterized maxRPC and adaptive partition-one-AC can obtain significant speed-ups over arc consistency and over the full versions of maxRPC and partition-one-AC.
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Dates et versions

hal-01711595 , version 1 (19-02-2018)

Identifiants

Citer

Amine Balafrej, Christian Bessiere, Anastasia Paparrizou, Gilles Trombettoni. Adapting Consistency in Constraint Solving. Christian Bessiere; Luc De Raedt; Lars Kotthoff; Siegfried Nijssen; Barry O'Sullivan; Dino Pedreschi. Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach, 10101, Springer, pp.226-253, 2016, Lecture Notes in Computer Science, 978-3-319-50136-9. ⟨10.1007/978-3-319-50137-6_9⟩. ⟨hal-01711595⟩
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