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Within the framework proposed in this paper, we address the issue ofextending the certain networks to a fuzzy certain networks in order to copewith a vagueness and limitations of existing models for decision underimprecise and uncertain knowledge. This paper proposes a framework thatcombines two disciplines to exploit their own advantages in uncertain andimprecise knowledge representation problems. The framework proposed is apossibilistic logic based one in which Bayesian nodes and their properties arerepresented by local necessity-valued knowledge base. Data in properties areinterpreted as set of valuated formulas. In our contribution possibilisticBayesian networks have a qualitative part and a quantitative part, representedby local knowledge bases. The general idea is to study how a fusion of thesetwo formalisms would permit representing compact way to solve efficientlyproblems for knowledge representation. We show how to apply possibility andnecessity measures to the problem of knowledge representation with large scaledata. On the other hand fuzzification of crisp certainty degrees to fuzzyvariables improves the quality of the network and tends to bring smoothness androbustness in the network performance. The general aim is to provide a newapproach for decision under uncertainty that combines three methodologies:Bayesian networks certainty distribution and fuzzy logic.

Fuzzy Knowledge Representation Based on Possibilistic and Necessary Bayesian Networks