Artificial intelligence and neural networks: steps toward principled integration

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Academic Press, 1994 - Computers - 653 pages
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Traditional artificial intelligence and neural networks are generally considered appropriate for solving different types of problems. On the surface, these two approaches appear to be very different, but a growing body of current research is focused on how the strengths of each can be incorporated into the other and built into systems that include the best features of both. Artificial Intelligence and Neural Networks: Steps Toward Principled Integration is a critical examination of the key issues, underlying assumptions, and suggestions related to the reconciliation and principled integration of artificial intelligence and neural networks. With contributions from leading researchers in the field, this comprehensive text provides a thorough introduction to the basics of symbol processing, connectionist networks, and their integration. Numerous examples of the integration of artificial intelligence and neural networks for a variety of specific applications provide unique insight into this evolving area. Includes contributions from some of the leading researchers in this area Provides a complete introduction to the basics of symbol processing, connectionist networks, and their integration Includes examples of the integration of artificial intelligence and neural networks for a variety of specific applications, including vision and pattern recognition

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Contents

Horses of A Different Colour?
3
The Problems
21
Cooperative Computation
51
Copyright

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About the author (1994)

Vasant Honavar is Associate Professor of Computer Science, Bioinformatics and Computational Biology, and Neuroscience and Director of the Artificial Intelligence Research Laboratory at Iowa State University.

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