Tech report available by ftp

[honavar@iastate.edu]

The following technical report is available in postscript form
by anonymous ftp (courtesy Jordan Pollack of Ohio State Univ). 
Comments on the paper are welcome (please direct them to honavar at iastate.edu) 

----------------------------------------------------------------------

Symbol Processing Systems, Connectionist Networks, and
Generalized Connectionist Networks

Vasant Honavar				Leonard Uhr 
Department of Computer Science		Computer Sciences Department 
Iowa State University			University of Wisconsin-Madison 

		Technical Report #90-24, December 1990
		Department of Computer Science
		Iowa State University, Ames, IA 50011 

				Abstract

Many authors have suggested that SP (symbol processing) and CN 
(connectionist network) models offer radically, or even 
fundamentally, different paradigms for modeling intelligent behavior 
(see Schneider, 1987) and the design of intelligent systems. Others 
have argued that CN models have little to contribute to our efforts 
to understand intelligence (Fodor & Pylyshyn, 1988). 
A critical examination of the popular characterizations of SP and CN 
models suggests that neither of these extreme positions is justified.
There are many advantages to be gained by a synthesis of the best of 
both SP and CN approaches in the design of intelligent systems.
The Generalized connectionist networks (GCN) (alternately called 
generalized neuromorphic systems (GNS)) introduced in this paper provide 
a framework for such a synthesis.

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