  In this paper, we are trying to examine trade offs between fuzzy logic and
certain Bayesian networks and we propose to combine their respective advantages
into fuzzy certain Bayesian networks (FCBN), a certain Bayesian networks of
fuzzy random variables. This paper deals with different definitions and
classifications of uncertainty, sources of uncertainty, and theories and
methodologies presented to deal with uncertainty. 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 aim is
to provide a new approach for decision under uncertainty that combines three
methodologies: Bayesian networks certainty distribution and fuzzy logic. 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.
