Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.artint.2005.02.004
Title: An optimal coarse-grained arc consistency algorithm
Authors: Bessière, C.
Régin, J.-C.
Yap, R.H.C. 
Zhang, Y.
Keywords: Arc consistency
Constraint networks
Constraint programming systems
Non-binary constraints
Path consistency
Issue Date: 2005
Source: Bessière, C., Régin, J.-C., Yap, R.H.C., Zhang, Y. (2005). An optimal coarse-grained arc consistency algorithm. Artificial Intelligence 165 (2) : 165-185. ScholarBank@NUS Repository. https://doi.org/10.1016/j.artint.2005.02.004
Abstract: The use of constraint propagation is the main feature of any constraint solver. It is thus of prime importance to manage the propagation in an efficient and effective fashion. There are two classes of propagation algorithms for general constraints: fine-grained algorithms where the removal of a value for a variable will be propagated to the corresponding values for other variables, and coarse-grained algorithms where the removal of a value will be propagated to the related variables. One big advantage of coarse-grained algorithms, like AC-3, over fine-grained algorithms, like AC-4, is the ease of integration when implementing an algorithm in a constraint solver. However, fine-grained algorithms usually have optimal worst case time complexity while coarse-grained algorithms do not. For example, AC-3 is an algorithm with non-optimal worst case complexity although it is simple, efficient in practice, and widely used. In this paper we propose a coarse-grained algorithm, AC2001/3.1, that is worst case optimal and preserves as much as possible the ease of its integration into a solver (no heavy data structure to be maintained during search). Experimental results show that AC2001/3.1 is competitive with the best fine-grained algorithms such as AC-6. The idea behind the new algorithm can immediately be applied to obtain a path consistency algorithm that has the best-known time and space complexity. The same idea is then extended to non-binary constraints. © 2005 Elsevier B.V. All rights reserved.
Source Title: Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/39294
ISSN: 00043702
DOI: 10.1016/j.artint.2005.02.004
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