Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial intelligence: a new synthesis
Artificial intelligence: a new synthesis
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
A general scheme for automatic generation of search heuristics from specification dependencies
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Mini-buckets: A general scheme for bounded inference
Journal of the ACM (JACM)
Solving weighted CSP by maintaining arc consistency
Artificial Intelligence
No-commitment branch and bound search for distributed constraint optimization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
AND/OR search spaces for graphical models
Artificial Intelligence
AND/OR Branch-and-Bound search for combinatorial optimization in graphical models
Artificial Intelligence
Memory intensive AND/OR search for combinatorial optimization in graphical models
Artificial Intelligence
Interleaved depth-first search
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Efficient stochastic local search for MPE solving
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Unifying tree decompositions for reasoning in graphical models
Artificial Intelligence
Towards parallel non serial dynamic programming for solving hard weighted CSP
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
Journal of Artificial Intelligence Research
Value elimination: bayesian inference via backtracking search
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
The Symposium on Combinatorial Search
AI Communications - The Symposium on Combinatorial Search
Predicting the size of depth-first branch and bound search trees
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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One popular and efficient scheme for solving combinatorial optimization problems over graphical models exactly is depth-first Branch and Bound. However, when the algorithm exploits problem decomposition using AND/OR search spaces, its anytime behavior breaks down. This article (1) analyzes and demonstrates this inherent conflict between effective exploitation of problem decomposition (through AND/OR search spaces) and the anytime behavior of depth-first search (DFS), (2) presents a new search scheme to address this issue while maintaining desirable DFS memory properties, and (3) analyzes and demonstrates its effectiveness through comprehensive empirical evaluation. Our work is applicable to any problem that can be cast as search over an AND/OR search space.