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[ontolog-forum] [Fwd: ICSI Talk: Thursday, October 20]

To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>, "[cwe-talk]" <cwe-talk@xxxxxxxxxxxx>, "[mp-sofi-sd] forum" <mp-sofi-sd@xxxxxxxxxxx>
From: "Peter P. Yim" <peter.yim@xxxxxxxx>
Date: Mon, 17 Oct 2005 10:28:38 -0700
Message-id: <4353DF46.3030309@xxxxxxxx>
For those who are interested:    (01)

(a) in this specific topic    (02)

(b) in getting informed (and be subscribed to the relevant 
announcement lists), and    (03)

(c) of course, those who are physically close enough (San 
Francisco Bay Area folks essentially) to be at this very 
interesting seminar.    (04)

--    (05)
--- Begin Message ---
To: seminars@xxxxxxxxxxxxxxxxx
From: Leah Hitchcock <leahh@xxxxxxxxxxxxxxxxx>
Date: Mon, 17 Oct 2005 09:26:25 -0700 (PDT)
Message-id: <Pine.GSO.4.60.0510170920430.3146@xxxxxxxxxxxxxxxxxxxxxxxx>
ICSI is pleased to present the following talk:    (01)

Computation with Information Described in Natural Language? The Concept of 
Generalized-Constraint-Based Computation    (02)

Presented by Professor Lotfi A. Zadeh, UC Berkeley EECS and the Berkeley 
Initiative in Soft Computing (BISC)    (03)

Thursday, October 20, 2005
4:00 p.m.
ICSI Main Lecture Hall
1947 Center Street, Suite 600
Berkeley, CA    (04)

Abstract:    (05)

What is computation with information described in natural language? Here 
are simple examples. I am planning to drive from Berkeley to Santa 
Barbara, with stopover for lunch in Monterey. It is about 10 am. It will 
probably take me about two hours to get to Monterey and about an hour to 
have lunch. From Monterey, it will probably take me about five hours to 
get to Santa Barbara. What is the probability that I will arrive in Santa 
Barbara before about six pm? Another simple example: A box contains about 
twenty balls of various sizes. Most are large. What is the number of small 
balls? What is the probability that a ball drawn at random is neither 
small nor large? Another example: A function, f, from reals to reals is 
described as: If X is small then Y is small; if X is medium then Y is 
large; if X is large then Y is small. What is the maximum of f?    (06)

Computation with information described in natural language, or 
NL-computation for short, is a problem of intrinsic importance because 
much of human knowledge is described in natural language. It is safe to 
predict that as we move further into the age of machine intelligence and 
mechanized decision-making, NL-computation will grow in visibility and 
importance.    (07)

Computation with information described in natural language cannot be dealt 
with through the use of machinery of natural language processing. The 
problem is semantic imprecision of natural languages. More specifically, a 
natural language is basically a system for describing perceptions. 
Perceptions are intrinsically imprecise, reflecting the bounded ability of 
sensory organs, and ultimately the brain, to resolve detail and store 
information. Semantic imprecision of natural languages is a concomitant of 
imprecision of perceptions.    (08)

Our approach to NL-computation centers on what is referred to as 
generalized-constraint-based computation, or GC-computation for short. A 
generalized constraint is expressed as X isr R, where X is the constrained 
variable, R is a constraining relation and r is an indexical variable 
which defines the way in which R constrains X. The principal constraints 
are possibilistic, veristic, probabilistic, usuality, random set, fuzzy 
graph and group. Generalized constraints may be combined, qualified, 
propagated, and counter propagated, generating what is called the 
Generalized Constraint Language, GCL. The key underlying idea is that 
information conveyed by a proposition may be represented as a generalized 
constraint, that is, as an element of GCL.    (09)

In our approach, NL-computation involves two modules: (a) Precisiation 
module; and (b) Computation module. The meaning of an element of a natural 
language, NL, is precisiated through translation into GCL and is expressed 
as a generalized constraint. An object of precisiation, p, is referred to 
as precisiend, and the result of precisiation, p*, is called a precisiand. 
Usually, a precisiend is a proposition or a concept. A precisiend may have 
many precisiands. Definition is a form of precisiation. A precisiand may 
be viewed as a model of meaning. The degree to which the intension 
(attribute-based meaning) of p* approximates to that of p is referred to 
as cointension. A precisiand, p*, is cointensive if its cointension with p 
is high, that is, if p* is a good model of meaning of p.    (010)

The Computation module serves to deduce an answer to a query, q. The first 
step is precisiation of q, with precisiated query, q*, expressed as a 
function of n variables u1, ...,un. The second step involves precisiation 
of query-relevant information, leading to a precisiand which is expressed 
as a generalized constraint on u1, ..., un. The third step involves an 
application of the extension principle, which has the effect of 
propagating the generalized constraint on u1, ...,un to a generalized 
constraint on the precisiated query, q*. Finally, the constrained q* is 
interpreted as the answer to the query and is retranslated into natural 
language.    (011)

The generalized-constraint-based computation approach to NL-computation 
opens the door to a wide-ranging enlargement of the role of natural 
languages in scientific theories. Particularly important application areas 
are decision-making with information described in natural language, 
economics, risk assessment, qualitative systems analysis, search, 
question-answering and theories of evidence.    (012)

ICSI Seminars mailing list
http://mailman.ICSI.Berkeley.EDU/mailman/listinfo/seminars-list    (013)

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