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Proceedings Paper

Artificial symbols and the essence of intelligent computing
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Paper Abstract

A challenge for intelligent computing is translating the skills of innovation into mathematical theory and persistent learning algorithms. Computational intelligence differs from artificial intelligence in that artificial intelligence reasons over symbols while computational intelligence reasons over sub-symbolic data and information. Natural symbos arise from shared human experiences. The creative quality of human interaction suggests symbol generation involves a collection of cooperative agents capable of representing relative experience, negotiating innovation, and---finally---building consensus. As hybrids of sub-symbolic and symbolic reasoning become the norm, it is necessary to formalize the design and evaluation of artificial symbols. In this paper, we delineate the difference between sub-symbolic patterns and symbolic experience. Further, we propose fundamental theory supporting the autonomous construction of artificial symbols which---we assert---is the ultimate culmination of an intelligent computation. We apply this theory to model selection among neural networks.

Paper Details

Date Published: 4 August 2003
PDF: 12 pages
Proc. SPIE 5103, Intelligent Computing: Theory and Applications, (4 August 2003); doi: 10.1117/12.487485
Show Author Affiliations
Amy L. Magnus, Defense Threat Reduction Agency (United States)
Mark E. Oxley, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 5103:
Intelligent Computing: Theory and Applications
Kevin L. Priddy; Peter J. Angeline, Editor(s)

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