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Subject: talk announcement
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From: dlm@research.att.com
Date: Fri, 1 Jul 88 09:10 EDT
>From: allegra!dlm (D.L.McGuinness)
To: arpa!mc.lcs.mit.edu!AIList
Subject: talk announcement


Title:	Intermediate Mechanisms For Activation Spreading
	or
        Why can't neural networks talk to expert systems?

Speaker:Jim Hendler
        University of Maryland Institute for Advanced Computer Studies
        University of Maryland, College Park

Date:	Tuesday, July 19
Time: 	1:30
Place:	AT&T Bell Laboratories - Murray Hill  3D-473

Abstract:

               Spreading activation,  in  the  form  of  computer
          models and cognitive theories, has recently been under-
          going a resurgence of interest in the cognitive science
          and  AI  communities.  Two competing schools of thought
          have been forming.  One technique concentrates  on  the
          spreading  of  symbolic information through an associa-
          tive knowledge representation.  The other technique has
          focused on the passage of numeric information through a
          network.  In this talk we show  that  these  two  tech-
          niques can be merged.

               We show how an ``intermediate  level''  mechanism,
          that of symbolic marker-passing, can be used to provide
          a limited form of interaction between traditional asso-
          ciative networks and subsymbolic networks.  We describe
          the marker-passing technique,  show  how  a  notion  of
          microfeatures  can  be  used  to allow similarity based
          reasoning,  and  demonstrate  that  a  back-propogation
          learning  algorithm  can  build  the  necessary  set of
          microfeatures from a  well-defined  training  set.   We
          discuss  several problems in natural language and plan-
          ning research and show how the hybrid system  can  take
          advantage  of inferences that neither a purely symbolic
          nor a purely connectionist system can make at present.

Sponsor:  Diane Litman (allegra!diane)