Uncertain and approximate knowledge representation to reasoning on classification with a fuzzy networks based system

Abstract

The approach described allows one to use the fuzzy object based representation of imprecise and uncertain knowledge. This representation has a great practical interest due to the possibility to realize reasoning on classification with a fuzzy semantic network based system. The approach describes the theoretical aspects of the architecture of the whole experimental AI system we built in order to provide effective online assistance to users of new technological systems: the understanding of "how it works" and "how to complete tasks" from queries in quite natural languages. In our model, procedural semantic networks are used to describe the knowledge of an "ideal" expert while fuzzy sets are used both to describe the approximate and uncertain knowledge of novice users in fuzzy semantic networks which intervene to match fuzzy labels of a query with categories from our "ideal" expert.