Abstract
In possibility theory, there are two kinds of possibilistic causal networks depending if the possibilistic conditioning is based on the minimum or the product operator. Product-based possibilistic networks share the same practical and theoretical features as Bayesian networks. In this paper, we focus on min-based causal networks and propose a propagation algorithm for such networks. The basic idea is first to transform the initial network only into a moral graph. Then, two different procedures, called stabilization and checking consistency, are applied to compute the possibility degree of any variable of interest given some evidence.
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© 2001 Springer-Verlag Berlin Heidelberg
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Amo, N.B., Benferhat, S., Mellouli, K. (2001). A Two-Steps Algorithm for Min-Based Possibilistic Causal Networks. In: Benferhat, S., Besnard, P. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2001. Lecture Notes in Computer Science(), vol 2143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44652-4_24
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DOI: https://doi.org/10.1007/3-540-44652-4_24
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