Abstract
Min-based possibilistic networks, which are compact representations of possibility distributions, are powerful tools for representing and reasoning with uncertain and incomplete information in the possibility theory framework. Inference in these graphical models has been recently the focus of several researches, especially under compilation. It consists in encoding the network into a Conjunctive Normal Form (CNF) base and compiling this latter to efficiently compute the impact of an evidence on variables. The encoding strategy of such networks can be either locally using local structure or globally using possibilistic local structure. This paper emphasizes on a comparative study between these strategies for compilation-based inference approaches in terms of CNF parameters, compiled bases parameters and inference time.
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Ayachi, R., Ben Amor, N., Benferhat, S. (2013). A Comparative Study of Compilation-Based Inference Methods for Min-Based Possibilistic Networks. In: van der Gaag, L.C. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2013. Lecture Notes in Computer Science(), vol 7958. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39091-3_3
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DOI: https://doi.org/10.1007/978-3-642-39091-3_3
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