Symbol Processing Systems, Connectionist Networks, and Generalized Connectionist Networks

Symbol Processing Systems, Connectionist Networks, and Generalized Connectionist Networks (1990)

by Vasant Honavar , Leonard Uhr

Venue:	IN S. GOONATILAKE AND S.KHEBBAL, EDITORS INTELLIGENT HYBRID SYSTEMS
Citations:	9 - 6 self

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Datum	Value	Source
TITLE	Symbol Processing Systems, Connectionist Networks, and Generalized Connectionist Networks	INFERENCE
AUTHOR NAME	Vasant Honavar	user correction
AUTHOR AFFIL	Department of Computer Science; Iowa State University	user correction
AUTHOR NAME	Leonard Uhr	user correction
AUTHOR AFFIL	Computer Sciences Department; University of Wisconsin-Madison	user correction
AUTHOR ADDR	IA 50011	user correction
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.	user correction
YEAR	1990	INFERENCE
VENUE	IN S. GOONATILAKE AND S.KHEBBAL, EDITORS INTELLIGENT HYBRID SYSTEMS	user correction
VENUE TYPE	TECHREPORT	INFERENCE
PAGES	177--208	INFERENCE
TECH	Tech. Rep. 90--23	INFERENCE
CITATIONS	83 found	ParsCit 1.0