Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Maintaining reversible DAC for Max-CSP
Artificial Intelligence
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Radio Link Frequency Assignment
Constraints
Arc Consistency for Soft Constraints
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Boosting Search with Variable Elimination
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Specializing Russian Doll Search
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Directed Arc Consistency Preprocessing
Constraint Processing, Selected Papers
Node and arc consistency in weighted CSP
Eighteenth national conference on Artificial intelligence
Constraint solving over semirings
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Russian doll search for solving constraint optimization problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Russian Doll Search (RDS) is a clever procedure to solve overconstrainedpro blems. RDS solves a sequence of nested subproblems, each including one more variable than the previous, until the whole problem is solved. Specialized RDS (SRDS) solves each subproblem for every value of the new variable. SRDS lower boundi s better than RDS lower bound, causing a higher efficiency. A natural extension is Full Specialized RDS (FSRDS), which solves each subproblem for every value of every variable. Although FSRDS lower boundi s better than the SRDS one, the extra work performedb y FSRDS renders it inefficient. However, much of the useless work can be avoided. With this aim, we present Opportunistic Specialization in RDS (OSRDS), an algorithm that lies between SRDS and FSRDS. In addition to specialize the values of one variable, OSRDS specializes some values of other variables that look promising to increase the lower boundi n the current distribution of inconsistency counts. Experimental results on random and real problems show the benefits of this approach.