  A new approach for uncertainty management for fuzzy, rule based decision
support systems is proposed: The domain expert's knowledge is expressed by a
set of rules that frequently refer to vague and uncertain propositions. The
certainty of propositions is represented using intervals [a, b] expressing that
the proposition's probability is at least a and at most b. Methods and
techniques for computing the overall certainty of fuzzy compound propositions
that have been defined by using logical connectives 'and', 'or' and 'not' are
introduced. Different inference schemas for applying fuzzy rules by using modus
ponens are discussed. Different algorithms for combining evidence that has been
received from different rules for the same proposition are provided. The
relationship of the approach to other approaches is analyzed and its problems
of knowledge acquisition and knowledge representation are discussed in some
detail. The basic concepts of a rule-based programming language called PICASSO,
for which the approach is a theoretical foundation, are outlined.
