Efficient algorithms for combinatorial problems on graphs with bounded, decomposability—a survey
BIT - Ellis Horwood series in artificial intelligence
Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Tree clustering for constraint networks (research note)
Artificial Intelligence
An optimal backtrack algorithm for tree-structured constraint satisfaction problems
Artificial Intelligence
Backtracking techniques for the job shop scheduling constraint satisfaction problem
Artificial Intelligence - Special volume on planning and scheduling
Compiling constraint satisfaction problems
Artificial Intelligence
A polynomial-time tree decomposition to minimize congestion
Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures
Hybrid backtracking bounded by tree-decomposition of constraint networks
Artificial Intelligence
A Tree-Decomposition Approach to Protein Structure Prediction
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Journal of Artificial Intelligence Research
Backdoors to typical case complexity
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning cluster-based structure to solve constraint satisfaction problems
Annals of Mathematics and Artificial Intelligence
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In this paper we present a novel approach to solving Constraint Satisfaction Problems whose constraint graphs are highly clustered and the graph of clusters is close to being acyclic. Such graphs are encountered in many real world application domains such as configuration, diagnosis, model-based reasoning and scheduling. We present a class of variable ordering heuristics that exploit the clustered structure of the constraint network to inform search. We show how these heuristics can be used in conjunction with nogood learning to develop efficient solvers that can exploit propagation based on either forward checking or maintaining arc-consistency algorithms. Experimental results show that maintaining arc-consistency alone is not competitive with our approach, even if nogood learning and a well known variable ordering are incorporated. It is only by using our cluster-based heuristics can large problems be solved efficiently. The poor performance of maintaining arc-consistency is somewhat surprising, but quite easy to explain.