Relaxation labelling algorithms-a review
Image and Vision Computing
Relaxation and neural learning: points of convergence and divergence
Journal of Parallel and Distributed Computing - Neural Computing
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Analysis of Stochastic Automata Algorithm for Relaxation Labeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Machine Learning Approach to POS Tagging
Machine Learning
Computing lower bound for MAX-CSP problems
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
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We present a optimization formulation for discrete binary CSP, based on the construction of a continuous function A(P) whose global maximum represents the best possible solution for that problem. By the best possible solution we mean either (i) a solution of the problem, if it is solvable, or (ii) a partial solution violating a minimal number of constraints, if the problem is unsolvable. This approach is based on relaxation labeling techniques used to enforce consistency in image interpretation. We have used a projected gradient ascent algorithm to maximize A(P) on the n-queens problem obtaining good results but with a high computational cost. To elude this problem, we have developed a heuristic for variable and value selection inspired in the direction in which A(P) is maximized. We have tested this heuristic with forward checking on several classes of CSP.