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WORLDCOMP'10 Featured Keynote Lecture - Prof. Lotfi A. Zadeh

Last modified 2010-01-31 11:45

Computing With Words and Perceptions—A Paradigm Shift
Prof. Lotfi A. Zadeh
Father of Fuzzy Logic
Professor EECS and Director BISC
University of California, Berkeley, USA
Fellow of IEEE, ACM, AAAS, AAAI, and IFSA
Member, National Academy of Engineering
Recipient of 26 honorary doctorates and numerous IEEE, ACM, and other distinguished awards.

http://www.cs.berkeley.edu/~zadeh/
http://coe.berkeley.edu/forefront/spring2006/zadeh.html
http://www-bisc.cs.berkeley.edu/

Date: July 12, 2010
Time: TBA
Location: Lance Burton Theater


Abstract

    There are many misconceptions about what Computing with Words (CW) is and what it has to offer. A common misconception is that CW is closely related to natural language processing. In reality, this is not the case. More importantly, at this juncture what is widely unrecognized is that moving from computation with numbers to computation with words has the potential for evolving into a basic paradigm shift—a paradigm shift which would open the door to a wide-ranging enlargement of the role of natural languages in scientific theories.

    In essence, CW is a system of computation which adds to traditional systems of computation two important capabilities: (a) the capability to precisiate the meaning of words and propositions drawn from natural language; and (b) the capability to reason and compute with precisiated words and propositions.

    As a system of computation, a CW-based model, or simply CW-model, has three principal components. (a) A question, Q, of the form: What is the value of a variable, Y? (b) An information set, I=(p1, ..., pn), where the pi, i=(1, ..., n), are propositions which individually or collectively are carriers of information about the value of Y, that is, are question-relevant. One or more of the pi may be drawn from world knowledge. A proposition, pi, plays the role of an assignment statement which assigns a value, vi, to a variable, Xi, in pi. Equivalently, pi may be viewed as an answer to the question: What is the value of Xi? Xi and vi, may be explicit or implicit. A proposition, pi, may be unconditional or conditional, expressed as an if-then rule. Basically, an assignment statement constrains the values which Xi is allowed to take. To place this in evidence, Xi and vi are referred to as the constrained variable and the constraining relation, respectively. More concretely, what this implies is that the meaning of a proposition, p, may be represented as a generalized constraint, X isr R, in which X is the constrained variable, R is the constraining relation and r defines the modality of the constraint, that is, the way in which R constrains X. When vi is a word or a combination of words, Xi is referred to as a linguistic variable, with vi being its linguistic value. When it is helpful to stress that pi assigns a value to a variable, pi is referred to as a valuation. Correspondently, the information set, I, is referred to as a valuation system, V.

    The third component is an aggregation function, f, which relates Y to the Xi.

      Y=f(X1, ..., Xn)

    The principal difference between CW and conventional systems of computation is that CW allows inclusion in the information set, I, of propositions expressed in a natural language, that is, linguistic valuations. Legalization of linguistic valuations has important implications. First, it greatly enhances the capability of computational methodologies to deal with imperfect information, that is, information which in one or more respects is imprecise, uncertain, incomplete, unreliable, vague or partially true. In realistic settings, such information is the norm rather than exception. Second, in cases in which there is a tolerance for imprecision, linguistic valuations serve to exploit the tolerance for imprecision through the use of words in place of numbers. And third, linguistic valuations are close to human reasoning and thus facilitate the design of systems which have a high level of machine intelligence, that is, high level of MIQ (machine IQ).

    What does Computing with Words have to offer? The answer rests on two important tools which are provided by the machinery of fuzzy logic. The first tool is a formalism for mm-precisiation of propositions expressed in a natural language through representation of the meaning of a proposition as a generalized constraint of the form X isr R, where as noted earlier X is the constrained variable, R is the constraining relation and r is the modality of the constraint (Zadeh 1986).

    The second tool is a formalism for computing with mm-precisiated propositions through propagation and counterpropagation of generalized constraints. The principal rule governing constraint propagation is the Extension Principle (Zadeh 1965, 1975). In combination, these two tools provide an effective formalism for computation with information described in a natural language. And it is these tools that serve as a basis for legalization of linguistic valuations.

    What is important to note is that the machinery of fuzzy if-then rules—a machinery which is employed in almost all applications of fuzzy logic—is a part of the conceptual structure of CW.

Biography

    LOTFI A. ZADEH is a Professor in the Graduate School, Computer Science Division, Department of EECS, University of California, Berkeley. In addition, he is serving as the Director of BISC (Berkeley Initiative in Soft Computing).

    Lotfi Zadeh is an alumnus of the University of Tehran, MIT and Columbia University. He held visiting appointments at the Institute for Advanced Study, Princeton, NJ; MIT, Cambridge, MA; IBM Research Laboratory, San Jose, CA; AI Center, SRI International, Menlo Park, CA; and the Center for the Study of Language and Information, Stanford University. His earlier work was concerned in the main with systems analysis, decision analysis and information systems. His current research is focused on fuzzy logic, computing with words and soft computing, which is a coalition of fuzzy logic, neurocomputing, evolutionary computing, probabilistic computing and parts of machine learning.

    Lotfi Zadeh is a Fellow of the IEEE, AAAS, ACM, AAAI, and IFSA. He is a member of the National Academy of Engineering and a Foreign Member of the Russian Academy of Natural Sciences, the Finnish Academy of Sciences, the Polish Academy of Sciences, Korean Academy of Science & Technology and the Bulgarian Academy of Sciences. He is a recipient of the IEEE Education Medal, the IEEE Richard W. Hamming Medal, the IEEE Medal of Honor, the ASME Rufus Oldenburger Medal, the B. Bolzano Medal of the Czech Academy of Sciences, the Kampe de Feriet Medal, the AACC Richard E. Bellman Control Heritage Award, the Grigore Moisil Prize, the Honda Prize, the Okawa Prize, the AIM Information Science Award, the IEEE-SMC J. P. Wohl Career Achievement Award, the SOFT Scientific Contribution Memorial Award of the Japan Society for Fuzzy Theory, the IEEE Millennium Medal, the ACM 2001 Allen Newell Award, the Norbert Wiener Award of the IEEE Systems, Man and Cybernetics Society, Civitate Honoris Causa by Budapest Tech (BT) Polytechnical Institution, Budapest, Hungary, the V. Kaufmann Prize, International Association for Fuzzy-Set Management and Economy (SIGEF), the Nicolaus Copernicus Medal of the Polish Academy of Sciences, the J. Keith Brimacombe IPMM Award, the Silicon Valley Engineering Hall of Fame, the Heinz Nixdorf MuseumsForum Wall of Fame, other awards and twenty-six honorary doctorates. He has published extensively on a wide variety of subjects relating to the conception, design and analysis of information/intelligent systems, and is serving on the editorial boards of over sixty journals.

Contact

    Prof. Lotfi A. Zadeh
    Department of EECS, University of California
    Berkeley, CA 94720-1776
    Telephone: 510-642-4959; Fax: 510-642-1712
    Email: zadeh@eecs.berkeley.edu

Academic Co-Sponsors
The Berkeley Initiative in Soft Computing (BISC)
University of California, Berkeley, USA

Collaboratory for Advanced Computing and Simulations (CACS)
University of Southern California, USA

Intelligent Data Exploration and Analysis Laboratory
University of Texas at Austin, Austin, Texas, USA

Harvard Statistics Department Genomics & Bioinformatics Laboratory
Harvard University, Cambridge, Massachusetts, USA

BioMedical Informatics & Bio-Imaging Laboratory
Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA


Hawkeye Radiology Informatics, Department of Radiology, College of Medicine, University of Iowa, Iowa, USA

Minnesota Supercomputing Institute
University of Minnesota, USA

Center for the Bioinformatics and Computational Genomics
Georgia Institute of Technology, Atlanta, Georgia, USA

Medical Image HPC & Informatics Lab (MiHi Lab)
University of Iowa, Iowa, USA


The University of North Dakota
Grand Forks, North Dakota, USA

Knowledge Management & Intelligent System Center (KMIS)
University of Siegen, Germany

UMIT, Institute of Bioinformatics and Translational Research, Austria
SECLAB of University of Naples Federico II
University of Naples Parthenope, & Second University of Naples, Italy

National Institute for Health Research
World Academy of Biomedical Sciences and Technologies
High Performance Computing for Nanotechnology (HPCNano)
Supercomputer Software Department (SSD), Institute of Computational Mathematics & Mathematical Geophysics, Russian Academy of Sciences

International Society of Intelligent Biological Medicine

The International Council on Medical and Care Compunetics

The UK Department for Business, Innovation and Skills

VMW Solutions Ltd.
Scientific Technologies Corporation
HoIP - Health without Boundaries

Space for Earth Foundation
Medical Modeling and Simulation Database (EVMS) of Eastern Virginia Medical School & the American College of Surgeons

Corporate Sponsor


Other Co-Sponsors
Manjrasoft (Cloud Computing Technology company), Melbourne, Australia

Hodges' Health


 


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