Probabilistic Choice Operators (PCOs) are convenient tools to model uncertainty in CP. They are useful to implement randomized algorithms and stochastic processes in the concurrent constraint framework. Their implementation is based on the random selection of a value inside a finite domain according to a given probability distribution. Unfortunately, the probabilistic choice of a PCO is usually delayed until the probability distribution is completely known. This is inefficient and penalizes their broader adoption in real-world applications. In this paper, we associate to PCO a filtering algorithm that prunes the variation domain of its random variable during constraint propagation. Our algorithm runs in O(n) where n denotes the size of the domain of the probabilistic choice. Experimental results show the practical interest of this approach. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Petit, M., & Gotlieb, A. (2007). Boosting probabilistic choice operators. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4741 LNCS, pp. 559–573). Springer Verlag. https://doi.org/10.1007/978-3-540-74970-7_40
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