Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Radio Link Frequency Assignment
Constraints
Hybrid backtracking bounded by tree-decomposition of constraint networks
Artificial Intelligence
Arc consistency for soft constraints
Artificial Intelligence
Solving weighted CSP by maintaining arc consistency
Artificial Intelligence
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
In the quest of the best form of local consistency for weighted CSP
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
AND/OR branch-and-bound for graphical models
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Value elimination: bayesian inference via backtracking search
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
SOFSEM'05 Proceedings of the 31st international conference on Theory and Practice of Computer Science
Exploiting Decomposition in Constraint Optimization Problems
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Mendelian error detection in complex pedigrees using weighted constraint satisfaction techniques
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
A Decomposition Technique for Max-CSP
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Tractable Optimization Problems through Hypergraph-Based Structural Restrictions
ICALP '09 Proceedings of the 36th Internatilonal Collogquium on Automata, Languages and Programming: Part II
Memory intensive AND/OR search for combinatorial optimization in graphical models
Artificial Intelligence
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Exploiting decomposition on constraint problems with high tree-width
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Russian Doll search with tree decomposition
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Soft arc consistency revisited
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
Advanced generic neighborhood heuristics for VNS
Engineering Applications of Artificial Intelligence
Structural tractability of constraint optimization
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Constraint optimization problems and bounded tree-width revisited
CPAIOR'12 Proceedings of the 9th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Modularity-based decompositions for valued CSP
Annals of Mathematics and Artificial Intelligence
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Several recent approaches for processing graphical models (constraint and Bayesian networks) simultaneously exploit graph decomposition and local consistency enforcing. Graph decomposition exploits the problem structure and offers space and time complexity bounds while hard information propagation provides practical improvements of space and time behavior inside these theoretical bounds. Concurrently, the extension of local consistency to weighted constraint networks has led to important improvements in branch and bound based solvers. Indeed, soft local consistencies give incrementally computed strong lower bounds providing inexpensive yet powerful pruning and better informed heuristics. In this paper, we consider combinations of tree decomposition based approaches and soft local consistency enforcing for solving weighted constraint problems. The intricacy of weighted information processing leads to different approaches, with different theoretical properties. It appears that the most promising combination sacrifices a bit of theory for improved practical efficiency.