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Continuity and Artificial Intelligence

Continuity and Artificial Intelligence The traditional approach to AI is limited because it fails to exploit continuity. The reliance on discrete logic has allowed the rapid initial advance of the subject, but constitutes an inherent deficiency. The limitations have become apparent, and are generally acknowledged by a revival of interest in neuralnet, or connectionist, techniques. This approach has become feasible because of technical developments allowing largescale parallel operation. Lessons can be learned by considering the evolution of natural intelligence. Recent studies from a biological viewpoint suggest that this has some unexpected features. The idea of concept formation should be extended to include quantifiable concepts, similar to the semantic variables of fuzzy set theory. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Kybernetes Emerald Publishing

Continuity and Artificial Intelligence

Kybernetes , Volume 20 (6): 12 – Jun 1, 1991

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References (27)

Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0368-492X
DOI
10.1108/eb005905
Publisher site
See Article on Publisher Site

Abstract

The traditional approach to AI is limited because it fails to exploit continuity. The reliance on discrete logic has allowed the rapid initial advance of the subject, but constitutes an inherent deficiency. The limitations have become apparent, and are generally acknowledged by a revival of interest in neuralnet, or connectionist, techniques. This approach has become feasible because of technical developments allowing largescale parallel operation. Lessons can be learned by considering the evolution of natural intelligence. Recent studies from a biological viewpoint suggest that this has some unexpected features. The idea of concept formation should be extended to include quantifiable concepts, similar to the semantic variables of fuzzy set theory.

Journal

KybernetesEmerald Publishing

Published: Jun 1, 1991

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