With the vision that machines can be rendered smarter, we have witnessed
for more than a decade tremendous engineering efforts to implement
intelligent sys- tems. These attempts involve emulating human reasoning,
and researchers have tried to model such reasoning from various points
of view. But we know precious little about human reasoning processes,
learning mechanisms and the like, and in particular about reasoning with
limited, imprecise knowledge. In a sense, intelligent systems are
machines which use the most general form of human knowledge together
with human reasoning capability to reach decisions. Thus the general
problem of reasoning with knowledge is the core of design methodology.
The attempt to use human knowledge in its most natural sense, that is,
through linguistic descriptions, is novel and controversial. The novelty
lies in the recognition of a new type of un- certainty, namely fuzziness
in natural language, and the controversality lies in the mathematical
modeling process. As R. Bellman [7] once said, decision making under
uncertainty is one of the attributes of human intelligence. When
uncertainty is understood as the impossi- bility to predict occurrences
of events, the context is familiar to statisticians. As such, efforts to
use probability theory as an essential tool for building intelligent
systems have been pursued (Pearl [203], Neapolitan [182)). The
methodology seems alright if the uncertain knowledge in a given problem
can be modeled as probability measures.
