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Symbolic Artificial Intelligence and Numeric Artificial Neural Networks: Towards a Resolution of the Dichotomy

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Computational Architectures Integrating Neural And Symbolic Processes

Part of the book series: The Springer International Series In Engineering and Computer Science ((SECS,volume 292))

Abstract

The attempt to understand intelligence entails building theories and models of brains and minds, both natural as well as artificial. From the earliest writings of India and Greece, this has been a central problem in philosophy. The advent of the digital computer in the 1950’s made this a central concern of computer scientists as well (Turing, 1950). The parallel development of the theory of computation (by John von Neumann, Alan Turing, EmilPost, Alonzo Church, Charles Kleene, Markov and others) provided a new set of tools with which to approach this problem — through analysis, design, and evaluation of computers and programs that exhibit aspects of intelligent behavior — such as the ability to recognize and classify patterns; to reason from premises to logical conclusions; and to learn from experience.

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  1. Department of Computer Science, Iowa State University, Ames, Iowa, 50011

    Vasant Honavar

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  1. The University of Alabama, Tuscaloosa, AL, USA

    Ron Sun

  2. Sun Microsystems Laboratories, Chelmsford, MA, USA

    Lawrence A. Bookman

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Honavar, V. (1995). Symbolic Artificial Intelligence and Numeric Artificial Neural Networks: Towards a Resolution of the Dichotomy. In: Sun, R., Bookman, L.A. (eds) Computational Architectures Integrating Neural And Symbolic Processes. The Springer International Series In Engineering and Computer Science, vol 292. Springer, Boston, MA. https://doi.org/10.1007/978-0-585-29599-2_11

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