BISC: Zadeh's Lecture, Friday April 5th

From: Masoud Nikravesh (nikravesh@cs.berkeley.edu)
Date: Thu Apr 04 2002 - 12:34:47 MEST

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    Berkeley Initiative in Soft Computing (BISC)
    *********************************************************************

    A New Direction in AI--Toward a Computational Theory of Perceptions

                                                            Lotfi Zadeh
                                 Dept. of Electrical Engineering and Computer
    Science
                                                            UC Berkeley

                                                       Friday April 5, 2002
                                                              11 - 12:30
                                                          5101 Tolman Hall
      

     Humans have a remarkable capability to perform a wide variety of physical and
    mental tasks without any measurements
     and any computations. Familiar examples are: parking a car, driving in city
    traffic, playing golf, cooking a meal, and
     summarizing a story. In performing such tasks, humans employ perceptions of
    time, direction, speed, shape, possibility,
     likelihood, truth, and other attributes of physical and mental objects.
    Reflecting the bounded ability of the human brain to
     resolve detail, perceptions are intrinsically imprecise. In more concrete
    terms, perceptions are f-granular, meaning that (a)
    the boundaries of perceived classes are unsharp; and (b) the values of
    attributes are granulated, with a granule being a
     clump of values (points, objects) drawn together by indistinguishability,
    similarity, proximity, or functionality. For
     example, the granules of age might be labeled very young, young, middle-age,
    old, very old, etc.
     F-granularity of perceptions puts them well beyond the reach of traditional
    methods of analysis based on predicate logic
     and/or probability theory. The computational theory of perceptions (CTP) that
    is outlined here adds to the armamentarium of
    AI a capability to compute and reason with perception-based information. The
    point of departure in CTP is the assumption
    that perceptions are described by propositions drawn from a natural language,
    e.g., it it is very unlikely that there will be a
    significant increase in the price of oil in the near future.
    In CTP, a proposition, p, is viewed as an answer to a question and the meaning
    of p is represented as a generalized
    constraint. To compute with perceptions, their descriptors are translated into
    what is called the Generalized Constraint
    Language (GCL). Then, goal-directed constraint propagation is employed to
    answer a given query. A concept that plays a
    key role in CTP is that of Precisiated Natural Language (PNL). The computational
    theory of perceptions suggests a new
    direction in AI--a direction that may enhance the ability of AI to deal with
    real-world problems in which decision-relevant
    information is a mixture of measurements and perceptions. What is not widely
    recognized is that many important problems in
    AI fall into this category.

    * 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 ONR
    N00014-00-1-0621, ONR Contract
    N00014-99-C-0298, NASA Contract NCC2-1006, NASA Grant NAC2-117, 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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