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Newsgroups: comp.ai
Subject: Re: What is "fuzzy logic"?
Message-ID: <1991Apr4.191217.17894@hawk.cs.ukans.edu>
From: spratt@hawk.cs.ukans.edu (Lindsey Spratt)
Date: Thu, 4 Apr 1991 19:12:17 GMT
References: <2278@lee.SEAS.UCLA.EDU> <1991Apr1.205421.8079@athena.cs.uga.edu> <4412@skye.ed.ac.uk>
Organization: University of Kansas Computer Science Dept
Keywords: fuzzy logic, expert systems
Lines: 33

I have spent some time working with fuzzy logic. There is an expert
system shell I implemented to explore some issues in this area.

MESS (Modest Expert System Shell, or My Expert System Shell) is a
backward-chaining rule-based system built on top of the LPA MacProlog
environment. In it, I use fuzzy truth values on the rules and the user
can associate fuzzy truth values with her answers. That is, a truth
value is a fuzzy set (or fuzzy data value) defined over the interval
[0,1]. This is instead of simply having a truth value of some
particular number (say 0.783)as the truth value associated with a rule
or answer. The fuzzy truth values are named, for ease of reference in
writing the rules and in answering questions. In this approach, one
can represent the "unknown" truth value as the fuzzy set in which all
truth values in the range [0,1] have a membership of 1.

Some interesting problems in this approach include defining an
ordering on fuzzy truth values and defining a similarity measure for
fuzzy truth values, in addition to the perhaps obvious problems of
defining the logical connectives and negation.

The similarity measure is used to report results to the user. The
processing of a query produces a result with some associated fuzzy
truth value. This associated truth value may not have exactly the same
membership function as any of the pre-defined (and named) fuzzy truth
values. Rather than report the membership function to the user, the
name of the most-similar predefined fuzzy truth value is reported
instead.

I'd like to add handling of fuzzy data values as well - so that one
can use "linguistic values" in reporting data. Things like "big" vs
"little".

Lindsey