Fuzzy sets were introduced by Zadeh (1965) as a means of representing and manipulating data that was not precise, but rather fuzzy. Fuzzy logic proΒ vides an inference morphology that enables approximate human reasoning capabilities to be applied to knowledge-based systems. The theory of fuzzy logic provides a mathematical strength to capture the uncertainties associΒ ated with human cognitive processes, such as thinking and reasoning. The conventional approaches to knowledge representation lack the means for repΒ resentating the meaning of fuzzy concepts. As a consequence, the approaches based on first order logic and classical probablity theory do not provide an appropriate conceptual framework for dealing with the representation of comΒ monsense knowledge, since such knowledge is by its nature both lexically imprecise and noncategorical. The developement of fuzzy logic was motivated in large measure by the need for a conceptual framework which can address the issue of uncertainty and lexical imprecision. Some of the essential characteristics of fuzzy logic relate to the following [242]. β’ In fuzzy logic, exact reasoning is viewed as a limiting case of apΒ proximate reasoning. β’ In fuzzy logic, everything is a matter of degree. β’ In fuzzy logic, knowledge is interpreted a collection of elastic or, equivalently, fuzzy constraint on a collection of variables. β’ Inference is viewed as a process of propagation of elastic conΒ straints. β’ Any logical system can be fuzzified. There are two main characteristics of fuzzy systems that give them better performance fΓΌr specific applications.