Abstract
This article deals with logical and graphical representations of uncertain information in a quantitative possibilistic theory framework. We first provide a deep analysis of relationships between these two forms of representational frameworks. Then, we provide a novel algorithm for reasoning with quantitative possibilistic logic. The algorithm exploits the syntactic relations between quantitative possibilistic logic bases and penalty logic bases. We provide experimental results that compare the propagation algorithm developed for the possibilistic product-based networks and the inference algorithm developed in this paper. quantitative possibilistic theory, penalty logic, uncertainty propagation algorithm.
Published Version
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