BISC: BISC Seminar, May 17, 2001

From: masoud nikravesh (nikraves@eecs.berkeley.edu)
Date: Tue May 15 2001 - 15:47:54 MET DST

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    Berkeley Initiative in Soft Computing (BISC)
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    Causality

    BISC Seminar

    Jerry R. Hobbs
    Artificial Intelligence Center
    SRI International
    Menlo Park, California

    Thursday, 17 May 2001
    380 Soda Hall
    4:00-5:00pm

    Abstract:

    We do things in the world by exploiting our knowledge of what causes
    what. But in trying to reason formally about causality, there is a
    difficulty: to reason with certainty
    we need complete knowledge of all the relevant events and circumstances,
    whereas in everyday reasoning tasks we need a more serviceable but
    looser notion that does
    not make such demands on our knowledge. In this work I introduce the
    notion of ``causal complex'' for the complete set of events and
    conditions necessary for the
    causal consequent to occur, and use the term ``cause'' for the
    makeshift, nonmonotonic notion we required for everyday tasks such as
    planning and language
    understanding. Like all interesting concepts, neither of these can be
    defined with necessary and sufficient conditions, but they can be more
    or less tightly constrained by
    necessary conditions or sufficient conditions. I discuss the issue of
    how we distinguish between what is in a causal complex from what is
    outside it, and within a causal
    complex, how we distinugish the eventualities that deserve to be called
    ``causes'' from those that do not, in particular circumstances. In
    addition, I discuss some general
    properties of causality, such as transitivity, and how related notions
    such as ``prevent'', ``enable'', and ``maintain'', can be defined in
    terms of ``cause''.

    Bio: Dr. Jerry R. Hobbs is a Principal Scientist in the Artificial
    Intelligence Center, SRI International, where he has worked since 1977.
    He has done considerable
    research in natural language processing, information extraction,
    knowledge representation, and the encoding of commonsense knowledge.

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