Symbol Processing Systems, Connectionist Networks, and Generalized Connectionist Networks (1990)
| Venue: | IN S. GOONATILAKE AND S.KHEBBAL, EDITORS INTELLIGENT HYBRID SYSTEMS |
| Citations: | 9 - 6 self |
BibTeX
@TECHREPORT{Honavar90symbolprocessing,
author = {Vasant Honavar and Leonard Uhr},
title = {Symbol Processing Systems, Connectionist Networks, and Generalized Connectionist Networks},
institution = {IN S. GOONATILAKE AND S.KHEBBAL, EDITORS INTELLIGENT HYBRID SYSTEMS},
year = {1990}
}
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
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.
