A sufficient condition for backtrack-bounded search
Journal of the ACM (JACM)
Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Tree clustering for constraint networks (research note)
Artificial Intelligence
Decomposing constraint satisfaction problems using database techniques
Artificial Intelligence
Conjunctive query containment revisited
Theoretical Computer Science - Special issue on the 6th International Conference on Database Theory—ICDT '97
Conjunctive-query containment and constraint satisfaction
Journal of Computer and System Sciences - Special issue on the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems
A comparison of structural CSP decomposition methods
Artificial Intelligence
Binary vs. non-binary constraints
Artificial Intelligence
Uniform Constraint Satisfaction Problems and Database Theory
Complexity of Constraints
Tractable Optimization Problems through Hypergraph-Based Structural Restrictions
ICALP '09 Proceedings of the 36th Internatilonal Collogquium on Automata, Languages and Programming: Part II
Pure Nash equilibria: hard and easy games
Journal of Artificial Intelligence Research
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We study the relationships among structural methods for identifying and solving tractable classes of Constraint Satisfaction Problems (CSPs). In particular, we first answer a long-standing question about the notion of biconnected components applied to an "optimal" reduct of the dual constraint-graph, by showing that this notion is in fact equivalent to the hinge decomposition method. Then, we give a precise characterization of the relationship between the treewidth notion applied to the hidden-variable encoding of a CSP and the same notion applied to some optimal reduct of the dual constraint-graph. Finally, we face the open problem of computing such an optimal reduct. We provide an algorithm that outputs an approximation of an optimal tree decomposition, and give a qualitative explanation of the difference between this graph-based method and more general hypergraph-based methods.