Efficient ReasoningWith FuzzyWords

Abstract

Dealing with coarse granular information is very attractive for several reasons: 1. The number of details in an application might be so large that processing is not feasible without abstracting from details. 2. As we see in spoken languages, for example, coarse granular information is often easier to understand than lots of details. 3. Detailed information is not always available. Fuzzy systems are a natural choice for processing coarse granular information, but unfortunately, most fuzzy systems suffer from two drawbacks. Although knowledge is formulated on a coarse granular level using fuzzy sets, most information processing algorithms operate on the details and are, therefore, computationally costly. Furthermore, the fuzzy results are not expressed with the predefined fuzzy sets that we used to describe fuzzy knowledge in a comprehensive way, and are therefore often difficult to understand. As a solution to these problems, we propose a methodology that represents and processes fuzzy information at the coarse granular level.