Many decision-making tasks are too complex to be understood
quantitatively, however, humans succeed by using knowledge that is
imprecise rather than precise. Fuzzy logic resembles human reasoning in
its use of imprecise informa- tion to generate decisions. Unlike
classical logic which requires a deep under- standing of a system, exact
equations, and precise numeric values, fuzzy logic incorporates an
alternative way of thinking, which allows modeling complex systems using
a higher level of abstraction originating from our knowledge and
experience. Fuzzy logic allows expressing this knowledge with subjective
concepts such as very big and a long time which are mapped into exact
numeric ranges. Since knowledge can be expressed in a more natural by
using fuzzy sets, many decision (and engineering) problems can be
greatly simplified. Fuzzy logic provides 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 un- certainties associated with
human cognitive processes, such as thinking and reasoning. The
conventional approaches to knowledge representation lack the means for
representating the meaning of fuzzy concepts. As a consequence, the
approaches based on first order logic do not provide an appropriate con-
ceptual framework for dealing with the representation of commonsense
knowl- edge, since such knowledge is by its nature both lexically
imprecise and non- categorical.
