Abstract
We show that from well-known MDDs like the one modeling a sum, and operations between MDDs we can define efficient propagators of some complex constraints, like a weighted sum whose values satisfy a normal law. In this way, we avoid defining ad-hoc filtering algorithms. We apply this idea to different dispersion constraints and on a new statistical constraint we introduce: the Probability Mass Function constraint. We experiment out approach on a real world application. The conjunction of MDDs clearly outperforms all previous methods.
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Perez, G., Régin, JC. (2017). MDDs are Efficient Modeling Tools: An Application to Some Statistical Constraints. In: Salvagnin, D., Lombardi, M. (eds) Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2017. Lecture Notes in Computer Science(), vol 10335. Springer, Cham. https://doi.org/10.1007/978-3-319-59776-8_3
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DOI: https://doi.org/10.1007/978-3-319-59776-8_3
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