Abstract
Besides search, complete inference methods can also be used to solve soft constraint problems. Their main drawback is the high spatial complexity. To improve its practical usage, we present an approach to decrease memory consumtion in tree decomposition methods, a class of complete inference algorithms. This approach, called function filtering, allows to detect and remove some tuples that appear to be consistent (with a cost below the upper bound) but that will become inconsistent (with a cost exceeding the upper bound) when extended to other variables. Using this idea, we have developed new algorithms CTEf, MCTEf and IMCTEf, standing for cluster, mini-cluster and iterative mini-cluster tree elimination with function filtering. We demonstrate empirically the benefits of our approach.
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References
Bertele, U., Brioschi, F.: Nonserial Dynamic Programming. AC. Press (1972)
Cabon, B., Givry, S., Verfaillie, G.: Anytime lower bounds for constraint violation minimization problems. In: Maher, M.J., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, pp. 117–131. Springer, Heidelberg (1998)
de Givry, S., Verfaillie, G., Schiex, T.: Bounding the optimum of constraint optimization problems. In: Proceedings of the 3th Conference on Principles and Practice of Constraint Programming, Schloss Hagenberg, Austria, pp. 405–419
Dechter, R.: Constraint Processing. Elsevier, Amsterdam (2003)
Dechter, R., Kask, K., Larrosa, J.: A general scheme for multiple lower bound computation in constraint optimization. In: Proceedings of the 6th Conference on Principles and Practice of Constraint Programming, pp. 346–360 (2001)
Dechter, R., Pearl, J.: Network-based heuristics for constraint satisfaction problems. Artificial Intelligence 34, 1–38 (1987)
Dechter, R., Pearl, J.: Tree clustering for constraints networks. Artifical Intelligence 38 (1989)
Dechter, R.: Bucket elimination: A unifying framework for reasoning. Artifical Intelligence 113, 41–85 (1999)
Larkin, D., Dechter, R.: Bayesian inference in the presence of determinism (2003)
Bensana, E., Lemaitre, M., Verfaillie, G.: Earth observation satellite management. Constraints 4, 293–299 (1999)
Larrosa, J.: Node and arc consistency in weighted csp. In: Proc. AAAI (2002)
Larrosa, J., Morancho, E., Niso, D.: On the practical applicability of bucket elimination: Still-life as a case study. Journal of Artificial Intelligence Research (2005)
Larrosa, J., Schiex, T.: Solving weighted csp by maintaining arc consistency. Artificial Intelligence 159 (2004)
Sanchez, M., Meseguer, P., Larrosa, J.: Improving the applicability of adaptive consistency. In: Proceedings of the 10th Conference on Principles and Practice of Constraint Programming, Toronto, Canda (2004)
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Sánchez, M., Larrosa, J., Meseguer, P. (2005). Tree Decomposition with Function Filtering. In: van Beek, P. (eds) Principles and Practice of Constraint Programming - CP 2005. CP 2005. Lecture Notes in Computer Science, vol 3709. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564751_44
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DOI: https://doi.org/10.1007/11564751_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29238-8
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