# BISC: Lotfi A. Zadeh: Logic colloquiums - PNL: Precisiated Natural Language

From: Masoud Nikravesh (nikravesh@eecs.berkeley.edu)
Date: Sun Jan 14 2001 - 02:47:18 MET

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Berkeley Initiative in Soft Computing (BISC)
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PNL: Precisiated Natural Language
Professor Lotfi A. Zadeh

4:00 - 5:00 pm
Friday: Jan 19, 2001
60 Evans Hall
Logic colloquiums

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 widely recognized is that adherence to this tradition carries a
steep price--the inability to exploit the richness of natural languages in a way
that lends itself to computation and automated reasoning.
In a significant departure from existing methods, the high expressive power of
natural languages is harnessed by a process termed precisiation. In essence, if
p is proposition in a natural language (NL), then precisiation of p results in a
representation of the meaning of p in the form of what is referred to as a
generalized constraint. In a generic form, a generalized constraint is expressed
as X isr R, where X is the constrained variable, R is the constraining relation
and r is a discrete-valued indexing variable whose values define the ways in
which R constrains X. In general, X, R, and r are implicit in p. Thus
precisiation of p involves explicitation and instantiation of X, R, and r.
The principal types of constraints and the associated values of r are the
following: possibilistic (r = blank); veristic (r = v); probabilistic (r = p);
usuality (r = u); random set (r = rs); fuzzy graph (r = fg); and Pawlak set (r =
ps). With these constraints serving as basic building blocks, composite
generalized constraints can be generated by combination, constraint propagation,
modification, and qualification. The set of all composite generalized
constraints and associated rules of generation and interpretation constitute the
Generalized Constraint Languages (GCL). Translation from NL to GLC is governed
by the constraint-centered semantics of natural languages (CSNL). Thus, through
CSNL, GCL serves as precisiation language for NL.
Precisiation Natural Language (PNL) is a subset of NL, which is equipped with
constraint-centered semantics and is translatable into GLC. By construction, GCL
is maximally expressive. In consequence, PNL is the largest subset of NL, which
admits precisiation. The expressive power of PNL is far greater than that of
conventional predicate-logic-based meaning-representation languages.
The concept of PNL opens the door to a significant enlargement of the role of
natural languages in scientific theories and, especially, in information
processing, decision, and control. In these and other realms, a particularly
important function that PNL can serve is that of a concept definition
language--a language that makes it possible to formulate precise definitions of
new concepts and redefine those existing concepts that do not provide a good fit
to reality.

```--
Dr. Masoud NikRavesh
Research Engineer - BT Senior Research Fellow
Chair: BISC Special Interest Group on Fuzzy Logic and Internet
Berkeley initiative in Soft Computing (BISC)
Computer Science Division- Department of EECS
University of California, Berkeley, CA 94720
Phone: (510) 643-4522 - Fax: (510) 642-5775
Email: nikravesh@cs.berkeley.edu
URL: http://www.cs.berkeley.edu/~nikraves/
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