Artificial intelligence and neural networks: steps toward principled integration
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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activation analog approach architecture Artificial Intelligence ARTMAP back-propagation behavior binary biological blob brain Cambridge classifier systems clause Cognitive Science complex compositionality Computer Science concept connectionism connectionist models connectionist networks connectionist systems constituents constraints context corresponding DETE discrete distributed representations domain theory dynamic encoding example extracted feature Figure finite-state fully distributed function fuzzy genetic algorithms GOFAI grammar Grossberg hidden units Hinton implemented inductive learning inference information processing input instance Integration interactions internal knowledge knowledge representation layer logic Machine Learning match mathematical McClelland memory Morgan Kaufmann NANN neural networks neurons nodes objects operations output parallel Parallel Distributed Processing pattern recognition Paul Smolensky performance prediction predictron problem receptive fields recurrent networks recurrent neural networks represent rules Rumelhart schema semantic sentence sequence simple simulations Smolensky specific strings structure symbolic symboloids syntactic task tion Touretzky training set Turing types values vector visual weights