The document discusses the goals of artificial intelligence and limitations of symbolic approaches. It proposes that integrating diverse theories like neural networks, probabilistic methods, and fuzzy techniques can result in a more powerful approach than opposing theories. Specifically, it advocates for the use of fuzzy sets for knowledge representation and fuzzy logic for inference under uncertainty. The combination of fuzzy and neural network techniques is also discussed as a way to get closer to the goals of artificial intelligence.

1. "FUZZYSets, Fuzzy Logic and the Goals of Artificial Intelligence"
Anca Ralescu
Laboratory for International Fuzzy Engineering, Siber Hegner Building 3F1, 89- 1 Yamashita-cho,
Naka-ku Yokohama 231, Japan
and
Computer Science Department, University of Cincinnati, Cincinnati, Ohio 4522 1, U.S.A.
The central issues in the study of intelligent systems We discuss the tradeoff in accuracy versus flexibility
include those of knowledge representation, leaning and and we argue that when immediate, practical results are of
reasoning. The treatment of these issues becomes more primary concem the usual desire for accuracy and formal
complex when uncertainty and imprecision must be taken treatment decreases.
into account.
In this lecture we investigate some of the goals of
artificial intelligence and the limitations of the purely
symbolic approach.
Altemative approaches including neural networks,
probabilistic methods, and fuzzy techniques have been
sought and experimented with. The lecture will focus on
the relevance of fuzzy logic to the treatment of knowledge
representation, learning and inference.
We propose that, rather than oppose diverse theories in
a fruitless confrontation, their integration can result in a
more powerful approach to the study of intelligent systems.
The use of fuzzy sets for knowledge representation, and
of fuzzy logic for inference under uncertainty (with or
without a probabilistic method as the problem in hand may
require) is illustrated. The advantage of combining fuzzy
and neural network techniques is also discussed. We point
out that a collection of new computing methods, globally
known as soft computing, may indeed lead us closer to the
goals of artificial intelligence.
From a converse point of view, starting from the goals
of intelligent systems we submit that the current fuzzy
methodology must also be augmented.
The underlying idea of OUT discussion is that fuzzy sets
theory, fuzzy logic and associated techniques provide an
excellent tool for interfacing the real world of
measurments, and the conceptual world embodied by
language.
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$03.000 1993 IEEE
0-81864260-2/93