**Subject: **abstract, talk

**From: **Michelle T. Lin (*michlin@cs.berkeley.edu*)

**Date: **Mon Jan 31 2000 - 20:58:37 MET

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Berkeley Initiative in Soft Computing (BISC)

*********************************************************************

NOTE: For technical reasons, this announcement was not sent on time.

Its purpose at this point is to provide information about the talk

that took place already.

-------------

Please note that the BISC Seminar lecture on "Toward an Enlargement of

Role of Natural Languages in Information Processing, Decision and

Control" by Lotfi A. Zadeh, is scheduled for Tuesday, January 25,

2000, in Room 405 Soda Hall, 4-5 pm.

Subsequent meetings by the Seminar will be held on Tuesdays, 4-5 pm,

in Room 310 Soda Hall.

-----------

Toward an Enlargement of the Role of Natural Languages in

Information Processing, Decision and Control

Lotfi A. Zadeh*

Abstract

It is a deep-seated tradition in science to view the use of

natural languages in scientific theories as a manifestation of

mathematical immaturity. The rationale for this tradition is that

natural languages are lacking in precision. However, what is not

recognized to the extent that it should, is that adherence to this

tradition carries a steep price. In particular, a direct consequence

is that existing scientific theories do not have the capability to

operate on perception-based information exemplified by "Most Finns are

honest." Such information is usually described in a natural language

and is intrinsically imprecise, reflecting a fundamental limitation on

the cognitive ability of humans to resolve detail and store

information. Because of their imprecision, perceptions do not lend

themselves to meaning-representation through the use of precise

methods based on predicate logic. This is the principal reason why

existing scientific theories do not have the capability to operate on

perception-based information.

In a related way, the restricted expressive power of

predicate-logic-based languages rules out the possibility of defining

many basic concepts such as causality, resemblance, smoothness and

relevance in realistic terms. In this instance, as in many others,

the price of precision is over-idealization and lack of robustness.

In a significant departure from existing methods, in the

approach which is described in this talk the high expressive power of

natural languages is harnessed by constructing what is called a

precisiated natural language (PNL). In essence, PNL is a subset of a

natural language (NL) -- a subset which is equipped with

constraint-centered semantics (CSNL) and is translatable into what is

called the Generalized Constraint Language (GCL). A concept which has

a position of centrality in GCL is that of a generalized constraint

expressed as X isr R, where X is the constrained variable, R is the

constraining relation, and isr (pronounced as ezar) is a variable

copula in which r is a discrete-valued variable whose value defines

the way in which R constrains X. Among the principal types of

constraints are possibilistic, veristic, probabilistic, random-set,

usuality, and fuzzy-graph constraints.

With these constraints serving as basic building blocks, more

complex (composite) constraints may be constructed through the use of

a grammar. The collection of composite constraints forms the

Generalized Constraint Language (GCL). The semantics of GCL is

defined by the rules that govern combination and propagation of

generalized constraints. These rules coincide with the rules of

inference in fuzzy logic (FL).

A key idea in PNL is that the meaning of a proposition, p, in

PNL may be represented as a generalized constraint which is an element

of GCL. Thus, translation of p into GCL is viewed as an explicitation

of X, R and r. In this sense, translation is equivalent to

explicitation.

The concept of a precisiated natural language and the

associated methodologies of computing with words and the computational

theory of perceptions open the door to a wide-ranging generalization

and restructuring of existing theories, especially in the realms of

information processing, decision and control. In this perspective,

what is very likely is that in coming years a number of basic concepts

and techniques drawn from linguistics will be playing a much more

important role in scientific theories than they do today.

---------

*Professor in the Graduate School and Director, Berkeley

Initiative in Soft Computing (BISC), Computer Science Division and the

Electronics Research Laboratory, Department of EECS, University of

California, Berkeley, CA 94720-1776; Telephone: 510-642-4959; Fax:

510-642-1712; E-Mail: zadeh@cs.berkeley.edu. Research supported in

part by NASA Grant NAC2-1177, ONR Grant N00014-96-1-0556, ONR Grant

FDN0014991035, ARO Grant DAAH 04-961-0341 and the BISC Program of UC

Berkeley.

---------------------------------

Toward an Enlargement of the Role of Natural Languages in

Information Processing, Decision and Control

Lotfi A. Zadeh*

Extended Abstract

It is a deep-seated tradition in science to view the use of

natural languages in scientific theories as a manifestation of

mathematical immaturity. The rationale for this tradition is that

natural languages are lacking in precision. However, what is not

recognized to the extent that it should, is that adherence to this

tradition carries a steep price. In particular, a direct consequence

is that existing scientific theories do not have the capability to

operate on perception-based information exemplified by "Most Finns are

honest." Such information is usually described in a natural language

and is intrinsically imprecise, reflecting a fundamental limitation on

the cognitive ability of humans to resolve detail and store

information. Because of their imprecision, perceptions do not lend

themselves to meaning-representation through the use of precise

methods based on predicate logic. This is the principal reason why

existing scientific theories do not have the capability to operate on

perception-based information.

In a related way, the restricted expressive power of

predicate-logic-based languages rules out the possibility of defining

many basic concepts such as causality, resemblance, smoothness and

relevance in realistic terms. In this instance, as in many others,

the price of precision is over-idealization and lack of robustness.

In a significant departure from existing methods, in the

approach which is described in this talk the high expressive power of

natural languages is harnessed by constructing what is called a

precisiated natural language (PNL). In essence, PNL is a subset of a

natural language (NL) -- a subset which is equipped with

constraint-centered semantics (CSNL) and is translatable into what is

called the Generalized Constraint Language (GCL). A concept which has

a position of centrality in GCL is that of a generalized constraint

expressed as X isr R, where X is the constrained variable, R is the

constraining relation, and isr (pronounced as ezar) is a variable

copula in which r is a discrete-valued variable whose value defines

the way in which R constrains X. Among the principal types of

constraints are the following: possibilistic constraint, r=blank,

with R playing the role of the possibility distribution of X; veristic

constraint, r=v, in which case R is the verity (truth) distribution of

X; probabilistic constraint, r=p, in which case X is a random variable

and R is its probability distribution; r=rs, in which case X is a

fuzzy-set-valued random variable (fuzzy random set) and R is its

fuzzy-set-valued probability distribution; and fuzzy-graph constraint,

r=fg, in which case X is a fuzzy-set-valued variable and R is its

fuzzy-set-valued possibility distribution.

With these constraints serving as basic building blocks, which

are analogous to terminal symbols in a formal language, more complex

(composite) constraints may be constructed through the use of a

grammar. Simple examples of composite constraints are: X isr R and X

iss S; and, if X isr R then Y iss S, or, equivalently, Y iss S if X

isr R. The collection of composite constraints forms the Generalized

Constraint Language (GCL). The semantics of GCL is defined by the

rules that govern combination and propagation of generalized

constraints. These rules coincide with the rules of inference in

fuzzy logic (FL).

A key idea in PNL is that the meaning of a proposition, p, in

PNL may be represented as a generalized constraint which is an element

of GCL. Thus, translation of p into GCL is viewed as an explicitation

of X, R and r. In this sense, translation is equivalent to

explicitation. The translate of p in GCL is referred to as its

canonical form (CF(p)). These concepts form the core of what is

called constraint-oriented semantics of natural languages (CSNL).

In what ways does the concept of a precisiated natural

language serve to enlarge the role of natural languages in scientific

theories? Standing out in importance are the following.

a. By serving as a base for the methodologies of computing

with words (CW) and the computational theory of perceptions (CTP).

The importance of these methodologies derives from the fact that in

real-world settings decision-relevant information is frequently a

mixture of measurements and perceptions. Existing methods of decision

analysis are measurement-based and do not have the capability to

operate on perception-based information. The computational theory of

perceptions adds this capability to existing theories and thereby

enhances their ability to deal with realistic problems.

b. By making it possible to compute with words rather than

with numbers when numerical values of decision-relevant variables are

not known with sufficient precision to justify the use of

numerically-based methods.

c. By making it possible to define concepts that are

perception - rather than measurement-based. Examples of such concepts

are: the concept of a usual value of a random variable; the concept

of smoothness of a function; the concept of optimality in

multicriterion optimization; and the concept of relevance in database

search. In particular, in the context of decision analysis, it

becomes possible and, in many cases, advantageous, to define the

goals, constraints, dependencies, probabilities, utilities, and

risk-aversion in PNL rather than analytically or numerically. These

are but a few examples of ways in which PNL-based methods can be used

as addenda to existing numerically-based methods.

The concept of a precisiated natural language and the

associated methodologies of computing with words and the computational

theory of perceptions open the door to a wide-ranging generalization

and restructuring of existing theories, especially in the realms of

information processing, decision and control. In this perspective,

what is very likely is that in coming years a number of basic concepts

and techniques drawn from linguistics will be playing a much more

important role in scientific theories than they do today.

-------

*Professor in the Graduate School and Director, Berkeley

Initiative in Soft Computing (BISC), Computer Science Division and the

Electronics Research Laboratory, Department of EECS, University of

California, Berkeley, CA 94720-1776; Telephone: 510-642-4959; Fax:

510-642-1712; E-Mail: zadeh@cs.berkeley.edu. Research supported in

part by NASA Grant NAC2-1177, ONR Grant N00014-96-1-0556, ONR Grant

FDN0014991035, ARO Grant DAAH 04-961-0341 and the BISC Program of UC

Berkeley.

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