Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Algebraic decision diagrams and their applications
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
A comparison of structural CSP decomposition methods
Artificial Intelligence
SetA*: an efficient BDD-based heuristic search algorithm
Eighteenth national conference on Artificial intelligence
Hybrid backtracking bounded by tree-decomposition of constraint networks
Artificial Intelligence
Arc consistency for soft constraints
Artificial Intelligence
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
SPUDD: stochastic planning using decision diagrams
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Dynamic management of heuristics for solving structured CSPs
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
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Constraint optimization underlies many problems in AI. We present a novel algorithm for finite domain constraint optimization that generalizes branch-and-bound search by reasoning about sets of assignments rather than individual assignments. Because in many practical cases, sets of assignments can be represented implicitly and compactly using symbolic techniques such as decision diagrams, the set-based algorithm can compute bounds faster than explicitly searching over individual assignments, while memory explosion can be avoided by limiting the size of the sets. Varying the size of the sets yields a family of algorithms that includes known search and inference algorithms as special cases. Furthermore, experiments on random problems indicate that the approach can lead to significant performance improvements.