Abstract
AbstractPossibilistic 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.KeywordsBayesian NetworkConjunctive Normal FormPropositional VariablePossibility DistributionPossibility TheoryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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