A Generalized Cyclic-Clustering Approach for Solving Structured CSPs

We propose a new method for solving structured CSPs which generalizes and improves the Cyclic-Clustering approach [4]. First, the cutset and the tree-decomposition of the constraint network, which are used for taking advantage of the CSP structure, are computed independently of the notion of triangulated induced subgraph. Then, unlike Cyclic-Clustering, our method can try to solve the treedecomposition part of the problem without having assigned all the variables of the cutset. Regarding the solving of the tree-decomposition part, we use the BTD method [6] like in [7]. As BTD records and exploits structural (no)goods, we provide some conditions which make possible the use of structural (no)goods recorded during previous calls of BTD and we implement them in a dedicated version of BTD. By so doing, from a theoretical viewpoint, we can provide a theoretical time complexity bound related to parameters of the cutset and the tree-decomposition and, from a practical viewpoint we expect to d...

• Type: Journal
• Year: 2009
• Where: ICTAI
• Authors: Cédric Pinto, Cyril Terrioux
• Keywords: Artificial Intelligence, Constraint Ci, Constraint Satisfaction Problem, Finite Constraint Satisfaction, ICTAI 2009