paper

[Chris Lacher]

The following paper has been placed in the neuroprose archives under the
name 'lacher.rapprochement.ps.Z'. Retrieval, uncompress, and printing 
have been successfuly tested.


                        Expert Networks:  
     Paradigmatic Conflict, Technological Rapprochement^\dagger

                           R. C. Lacher
                     Florida State University
                         lacher at cs.fsu.edu

Abstract.  A rule-based expert system is demonstrated to have both a
symbolic computational network representation and a sub-symbolic
connectionist representation.  These alternate views enhance the usefulness
of the original system by facilitating introduction of connectionist
learning methods into the symbolic domain.  The connectionist representation
learns and stores metaknowledge in highly connected subnetworks and domain
knowledge in a sparsely connected expert network superstructure.  The total
connectivity of the neural network representation approximates that of real
neural systems which may be useful in avoiding scaling and memory stability
problems associated with some other connectionist models.

Keywords.  symbolic AI, connectionist AI, connectionism, neural networks,
learning, reasoning, expert networks, expert systems, symbolic models,
sub-symbolic models.

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^\dagger Paper given to the symposium "Approaches to Cognition", the
fifteenth annual Symposium in Philosophy held at the University of North
Carolina, Greensboro, April 5-7, 1991.