Abstract
In constraint satisfaction, local search is an incomplete method for finding a solution to a problem. Solving a general constraint satisfaction problem (CSP) is known to be NP-complete; so that heuristic techniques are usually used. The main contribution of this work is twofold: (i) a technique for de-composing a CSP into a DFS-tree CSP structure; (ii) an heuristic search technique for solving DFS-tree CSP structures. This heuristic search technique has been empirically evaluated with random CSPs. The evaluation results show that the behavior of our heuristic outperforms than the behavior of a centralized algorithm.
This work has been partially supported by the research projects TIN2004-06354-C02- 01 (Min. de Educacion y Ciencia, Spain-FEDER), FOM- 70022/T05 (Min. de Fomento, Spain), GV/2007/274 (Generalidad Valenciana) and by the Future and Emerging Technologies Unit of EC (IST priority - 6th FP), under contract no. FP6-021235-2 (project ARRIVAL).
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Abril, M., Salido, M.A., Barber, F. (2007). DFS-Tree Based Heuristic Search. In: Miguel, I., Ruml, W. (eds) Abstraction, Reformulation, and Approximation. SARA 2007. Lecture Notes in Computer Science(), vol 4612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73580-9_4
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DOI: https://doi.org/10.1007/978-3-540-73580-9_4
Publisher Name: Springer, Berlin, Heidelberg
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