Fuzzy Knowledge Representation Based on Possibilistic and Necessary
  Bayesian Networks
  Within the framework proposed in this paper, we address the issue of
extending the certain networks to a fuzzy certain networks in order to cope
with a vagueness and limitations of existing models for decision under
imprecise and uncertain knowledge. This paper proposes a framework that
combines two disciplines to exploit their own advantages in uncertain and
imprecise knowledge representation problems. The framework proposed is a
possibilistic logic based one in which Bayesian nodes and their properties are
represented by local necessity-valued knowledge base. Data in properties are
interpreted as set of valuated formulas. In our contribution possibilistic
Bayesian networks have a qualitative part and a quantitative part, represented
by local knowledge bases. The general idea is to study how a fusion of these
two formalisms would permit representing compact way to solve efficiently
problems for knowledge representation. We show how to apply possibility and
necessity measures to the problem of knowledge representation with large scale
data. On the other hand fuzzification of crisp certainty degrees to fuzzy
variables improves the quality of the network and tends to bring smoothness and
robustness in the network performance. The general aim is to provide a new
approach for decision under uncertainty that combines three methodologies:
Bayesian networks certainty distribution and fuzzy logic.
