Abstract
Possibilistic networks are important graphical tools for representing and reasoning under uncertain pieces of information. In possibility theory, there are two kinds of possibilistic networks depending if possibilistic conditioning is based on the minimum or on the product operator. This paper explores inference in product-based possibilistic networks using compilation. This paper also reports on a set of experimental results comparing product-based possibilistic networks and min-based possibilistic networks from a spatial point of view.
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Ayachi, R., Amor, N.B., Benferhat, S. (2012). Inference Using Compiled Product-Based Possibilistic Networks. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31718-7_49
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DOI: https://doi.org/10.1007/978-3-642-31718-7_49
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