Arc and path consistence revisited
Artificial Intelligence
Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Arc consistency for factorable relations
Artificial Intelligence
A generic arc-consistency algorithm and its specializations
Artificial Intelligence
Arc-consistency and arc-consistency again
Artificial Intelligence
Using constraint metaknowledge to reduce arc consistency computation
Artificial Intelligence
Radio Link Frequency Assignment
Constraints
Contradicting Conventional Wisdom in Constraint Satisfaction
PPCP '94 Proceedings of the Second International Workshop on Principles and Practice of Constraint Programming
Using inference to reduce arc consistency computation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Refining the basic constraint propagation algorithm
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Making AC-3 an optimal algorithm
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Propositional Satisfiability and Constraint Programming: A comparative survey
ACM Computing Surveys (CSUR)
Random constraint satisfaction: Easy generation of hard (satisfiable) instances
Artificial Intelligence
Theoretical analysis of singleton arc consistency and its extensions
Artificial Intelligence
Solving quantified constraint satisfaction problems
Artificial Intelligence
Domain filtering consistencies for non-binary constraints
Artificial Intelligence
Properties of tree convex constraints
Artificial Intelligence
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Inverse Consistencies for Non-binary Constraints
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Dynamic arc consistency for CSPs
International Journal of Knowledge-based and Intelligent Engineering Systems
Data structures for generalised arc consistency for extensional constraints
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A study of residual supports in arc consistency
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Nogood recording from restarts
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
AC3-OP: An Arc-Consistency Algorithm for Arithmetic Constraints
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
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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.