Tree clustering for constraint networks (research note)
Artificial Intelligence
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Nonserial Dynamic Programming
Radio Link Frequency Assignment
Constraints
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Existential arc consistency: getting closer to full arc consistency in weighted CSPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Unifying tree decompositions for reasoning in graphical models
Artificial Intelligence
Reasoning from last conflict(s) in constraint programming
Artificial Intelligence
Russian Doll search with tree decomposition
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Resource-aware junction trees for efficient multi-agent coordination
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Anytime AND/OR depth-first search for combinatorial optimization
AI Communications - The Symposium on Combinatorial Search
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We introduce a parallelized version of tree-decomposition based dynamic programming for solving difficult weighted CSP instances on many cores. A tree decomposition organizes cost functions in a tree of collection of functions called clusters. By processing the tree from the leaves up to the root, we solve each cluster concurrently, for each assignment of its separator, using a state-of-the-art exact sequential algorithm. The grain of parallelism obtained in this way is directly related to the tree decomposition used. We use a dedicated strategy for building suitable decompositions. We present preliminary results of our prototype running on a cluster with hundreds of cores on different decomposable real problems. This implementation allowed us to solve the last open CELAR radio link frequency assignment instance to optimality.