tech report available

[Trent Lange]

**********DO NOT FORWARD TO OTHER BBOARDS**************
**********DO NOT FORWARD TO OTHER BBOARDS**************
**********DO NOT FORWARD TO OTHER BBOARDS**************

The following tech report is now available:

         High-Level Inferencing in a Connectionist Network

                         Trent E. Lange
                         Michael G. Dyer

                Artificial Intelligence Laboratory
                    Computer Science Department
               University of California, Los Angeles
	              Los Angeles, CA  90024

                         UCLA-AI-89-12

   Connectionist models have had problems representing and apply-
  ing general knowledge rules that specifically require variables.
  This variable binding problem has barred them from performing the
  high-level inferencing necessary for planning, reasoning, and
  natural language understanding.  This paper describes ROBIN, a
  structured neural network model capable of high-level inferencing
  requiring variable bindings and rule application.  Variable bind-
  ings are handled by signatures -- activation patterns which uniquely
  identify the concept bound to a role.  Signatures allow multiple
  role-bindings to be propagated across the network in parallel for
  rule application and dynamic inference path instantiation.  Signa-
  tures are integrated within a connectionist semantic network struc-
  ture whose constraint-relaxation process selects between those
  newly-instantiated inferences.  This allows ROBIN to handle an area
  of high-level inferencing difficult even for symbolic models, that
  of resolving multiple constraints from context to select the best
  interpretation from among several alternative and possibly ambiguous
  inference paths.


This is a pre-print of a paper that will appear in Connection Science.

................
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report number UCLA-AI-89-12.