Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Tree clustering for constraint networks (research note)
Artificial Intelligence
Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
A Sufficient Condition for Backtrack-Free Search
Journal of the ACM (JACM)
Algorithmic Graph Theory and Perfect Graphs (Annals of Discrete Mathematics, Vol 57)
Algorithmic Graph Theory and Perfect Graphs (Annals of Discrete Mathematics, Vol 57)
Hybrid tractability of valued constraint problems
Artificial Intelligence
Journal of Artificial Intelligence Research
On guaranteeing polynomially bounded search tree size
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Partitioning based algorithms for some colouring problems
CSCLP'05 Proceedings of the 2005 Joint ERCIM/CoLogNET international conference on Constraint Solving and Constraint Logic Programming
Algorithms for the maximum hamming distance problem
CSCLP'04 Proceedings of the 2004 joint ERCIM/CoLOGNET international conference on Recent Advances in Constraints
The tractability of CSP classes defined by forbidden patterns
Journal of Artificial Intelligence Research
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In this paper, we present a method for improving search efficiency in the area of Constraint Satisfaction-Problems in finite domains. This method is based on the analysis of the "micro-structure" of a CSP. We call micro-structure of a CSP, the graph defined by the compatible relations between variablevalue pairs: vertices are these pairs, and edges are defined by pairs of compatible vertices. Given the micro-structure of a CSP, we can realize a preprocessing to simplify the problem with a decomposition of the domains of variables. So, we propose a new approach to problem decomposition in the field of CSPs, well adjusted in cases such as classical decomposition methods are without interest (i.e. when the constraint graph is complete). The method is described in the paper and a complexity analysis is presented, given theoretical justifications of the approach. Furthermore, two polynomial classes of CSPs are induced by this approach, the recognition of them being linear in the size of the instance of CSP considered.