A sufficient condition for backtrack-bounded search
Journal of the ACM (JACM)
Domain filtering consistencies
Journal of Artificial Intelligence Research
Optimal and suboptimal singleton arc consistency algorithms
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A greedy approach to establish singleton arc consistency
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Solving a Stochastic Queueing Control Problem with Constraint Programming
CPAIOR '07 Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Optimization of Simple Tabular Reduction for Table Constraints
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Solving a stochastic queueing design and control problem with constraint programming
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A constraint programming approach for solving a queueing control problem
Journal of Artificial Intelligence Research
A framework for decision-based consistencies
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
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Shaving algorithms, like singleton arc consistency (SAC), are currently receiving much interest. They remove values which are not part of any solution. This paper proposes an efficient shaving algorithm for enforcing stronger forms of consistency than SAC. The algorithm is based on the notion of weak k-singleton arc consistency, which is equal to sac if k=1 but stronger if k1. This paper defines the notion, explains why it is useful, and presents an algorithm for enforcing it. The algorithm generalises Lecoutre and Cardon's algorithm for establishing sac. Used as pre-processor for MAC it improves the solution time for structured problems. When run standalone for k1, it frequently removes more values than sac at a reasonable time. Our experimental results indicate that at the sac phase transition, it removes many more values than SAC-1 for k=16 in less time. For many problems from the literature the algorithm discovers lucky solutions. Frequently, it returns satisfiable csps which it proves inverse consistent if all values participate in a lucky solution.