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From: NICK@AI.AI.MIT.EDU (Nick Papadakis)
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Subject: Talk Announcement
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Date: 8 Jun 88 02:16:00 GMT
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From: research!dlm@research.att.com
Date: Tue, 7 Jun 88 00:13 EDT
>From: allegra!dlm (D.L.McGuinness)
To: research!arpa!mc.lcs.mit.edu!ailist
Subject: Talk Announcement

______________________________________________________________________

		TALK ANNOUNCEMENT


Speaker:	Mark Derthick - Dept. of CS, Carnegie Mellon University

Title: 		Mundane Reasoning

Date: 		Tuesday, June 7
Time: 		10:00
Place: 		AT&T Bell Laboratories 	MH 3D436 

Abstract:

Frames are a natural and powerful conception for organizing knowledge.
Yet in most well-defined frame-based knowledge representation systems,
such as KL-ONE, the knowledge base must be logically consistent, no
guesses are made to remedy incomplete knowledge bases, and they
sometimes fail to return answers in a reasonable time, even for
seemingly easy queries.  On the other hand are connectionist knowledge
representation systems, which are more robust in that they can be made
to always return an answer quickly, and knowledge is combined
evidentially.  Unfortunately these systems, if they have a well
defined formal semantics at all, have had much less expressive power
than symbolic systems.  The differing characteristics result from two
independent decisions.  First, the statistical technique of Maximum a
Posteriori estimation is used as a semantic foundation rather than
logical deduction.  Second, heuristic simplifications of the models
considered give rise to fast, but errorful behavior.  Having made this
distinction, it is possible to use the same powerful syntax of
symbolic systems, but interpret it statistically and implement it with
a connectionist network.  Although correct networks are exponentially
large, they serve as a basis from which architectural simplifications
can be made which preserve an intuitive connection to the formal theory.
The knowledge base must be tuned to alleviate errors caused by the
heuristic simplifications, so the system is intended for familiar
everyday situations in which past performance has been used for
training and in which the ramifications of wrong answers are not
serious enough to justify the exponential search time required for
provably correct behavior.

Sponsor: Ron Brachman & Deborah McGuinness (allegra!dlm)