Dual viewpoint heuristics for binary constraint satisfaction problems
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
A generic arc-consistency algorithm and its specializations
Artificial Intelligence
Using Auxiliary Variables and Implied Constraints to Model Non-Binary Problems
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Dual modelling of permutation and injection problems
Journal of Artificial Intelligence Research
Look-ahead value ordering for constraint satisfaction problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A Flexible Search Framework for CHR
Constraint Handling Rules
Guiding Search using Constraint-level Advice
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
A hyperheuristic approach to select enumeration strategies in constraint programming
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
Adaptive Branching for Constraint Satisfaction Problems
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Value ordering for finding all solutions: interactions with adaptive variable ordering
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Expert Systems with Applications: An International Journal
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In finding all solutions to a constraint satisfaction problem, or proving that there are none, with a search algorithm that backtracks chronologically and forms k-way branches, the order in which the values are assigned is immaterial. However, we show that if the values of a variable are assigned instead via a sequence of binary choice points, and the removal of the value just tried from the domain of the variable is propagated before another value is selected, the value ordering can affect the search effort. We show that this depends on the problem constraints; for some types of constraints, we show that the savings in search effort can be significant, given a good value ordering.