Extended Interval-valued Confidence for Inference of Knowware System Using Hybrid Logic

An important task in developing an intelligent system is to model and represent human knowledge and its uncertainty. There are various types of uncertainty, and randomness and fuzziness are among the most important. Handling these two types of uncertainty appearing simultaneously in a system can be critical to support real world applications. We have developed the Knowware System (KWS) as an intelligent tool to support application developers in constructing customized hybrid knowledge-based systems (KBSs) without requiring developers being familiar with relevant intelligent techniques. The interval-valued confidence (IVC) has been introduced to represent fuzzy truth of facts and knowledge in hybrid KBS constructed by the KWS, and the hybrid logic has been adopted for an extended rule-based reasoning in the KWS. As part of our continued work, in this article, we further define an extended interval-valued confidence (EIVC) to handle both fuzzy truth and randomness of facts and knowledge in the KWS inference under the hybrid logic, by representing probability as an uncertainty measure on fuzzy truth