🚀 go-pugleaf

Computer Science Colloquium
UC Berkeley

Weds., Oct. 11, 1995
4 -5 p.m.
Soda Hall Auditorium, Room 306

Lotfi A. Zadeh
Professor Emeritus and Director, Berkeley Initiative in Soft 
Computing (BISC), Computer Science Division, Department of EECS, 
University of California, Berkeley, CA  94720-1776. Tel: (510) 642-4959; 
fax: (510) 642-5775; email: zadeh@cs.berkeley.edu


Abstract

Computing - in its traditional sense - involves for the most
part manipulation of numbers. By contrast, humans usually employ words
in computing and reasoning, arriving at conclusions expressed as
words from premises expressed in a natural language.

By their nature, words are less precise than numbers. For this reason,
computing with words is generally less precise than computing with
numbers. This raises the question: What is the point of computing
with words in preference to computing with numbers?  There are two
major imperatives.  First, computing with words is a necessity when
the available information is too imprecise to justify the use of 
numbers.  And second, computing with words is advantageous when there 
is a tolerance for imprecision that can be exploited to achieve 
tractability, robustness, low solution cost and better rapport with 
reality.

In our approach, the point of departure in computing with words (CW)
is a collection of propositions expressed in a natural language, with
a proposition viewed as an implicit constraint on a variable. The
variables and the associated constraints are assumed to be expressed
as composite words, with a constraining word viewed as a label of a
granule, that is, a fuzzy set of points drawn together by similarity.
Through a process of explicitation based on test-score semantics, the
constrained variable and the constraining relation in a proposition
are identified, leading to what is called a canonical form. 
The primary function of canonical
forms is to place in evidence the fuzzy constraints that are implicit
in the premises. The constraints can assume a variety of forms, among 
which are possibilistic, probabilistic, conjunctive and
random-set types. For purposes of computation, the rules of inference
in fuzzy logic are employed to propagate the constraints from premises
to conclusions. Finally, the fuzzy constraints in conclusions are 
retranslated into propositions expressed in a natural language.

As a methodology, computing with words has wide-ranging implications on
both basic and applied levels. In many ways, CW represents a sharp
departure from the deep-seated tradition of according more respect to
numbers than to words.  The shift in paradigm which is implicit in CW
is discussed and the use of CW is illustrated by examples.