Uncertainty Management for Fuzzy Decision Support Systems
This work addresses uncertainty handling for domain experts using fuzzy decision support systems, but it appears incremental as it builds on existing fuzzy logic and rule-based methods without claiming major breakthroughs.
The paper tackles uncertainty management in fuzzy rule-based decision support systems by representing proposition certainty with probability intervals and introducing methods for computing compound proposition certainty, applying inference schemas, and combining evidence from multiple rules. It also analyzes relationships to other approaches and discusses knowledge acquisition and representation issues, with the approach serving as a theoretical foundation for the PICASSO programming language.
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.