Riassunto:
Computational Architectures Integrating Neural and Symbolic Processes: A Perspective on the State of the Art focuses on a currently emerging body of research. With the reemergence of neural networks in the 1980s with their emphasis on overcoming some of the limitations of symbolic AI, there is clearly a need to support some form of high-level symbolic processing in connectionist networks. As argued by many researchers, on both the symbolic AI and connectionist sides, many cognitive tasks, e.g. language understanding and common sense reasoning, seem to require high-level symbolic capabilities. How these capabilities are realized in connectionist networks is a difficult question and it constitutes the focus of this book.
Computational Architectures Integrating Neural and Symbolic Processes addresses the underlying architectural aspects of the integration of neural and symbolic processes. In order to provide a basis for a deeper understanding of existing divergent approaches and provide insight for further developments in this field, this book presents: (1) an examination of specific architectures (grouped together according to their approaches), their strengths and weaknesses, why they work, and what they predict, and (2) a critique/comparison of these approaches.
Computational Architectures Integrating Neural and Symbolic Processes is of interest to researchers, graduate students, and interested laymen, in areas such as cognitive science, artificial intelligence, computer science, cognitive psychology, and neurocomputing, in keeping up-to-date with the newest research trends. It is a comprehensive, in-depth introduction to this new emerging field.
Contenuti:
Foreword; M. Arbib. 1. An Introduction: On Symbolic Processing in Neural Networks; R. Sun. Part I: Localist Architectures. 2. Complex Symbol-Processing in Conposit, a Transiently Localist Connectionist Architecture; J.A. Barnden. 3. A Structured Connectionist Approach to Inferencing and Retrieval; T.E. Lange. 4. Hierarchical Architectures for Reasoning; R.C. Lacher, K.D. Nguyen. Part II: Distributed Architectures. 5. Subsymbolic Parsing of Embedded Structures; R. Miikkulainen. 6. Towards Instructable Connectionist Systems; D.C. Noelle, G.W. Cottrell. 7. An Internal Report for Connectionists; N.E. Sharkey, S.A. Jackson. Part III: Combined Architectures. 8. A Two-Level Hybrid Architecture for Structuring Knowledge for Commonsense Reasoning; R. Sun. 9. A Framework for Integrating Relational and Associational Knowledge for Comprehension; L.A. Bookman. 10. Examining a Hybrid Connectionist/Symbolic System for the Analysis of Ballistic Signals; C. Lin, J. Hendler. Part IV: Commentaries. 11. Symbolic Artificial Intelligence and Numeric Artificial Neural Networks: Towards a Resolution of the Dichotomy; V. Honavar. 12. Connectionist Natural Language Processing: a Status Report; M.G. Dyer. Appendix: Bibliography of Connectionist Models with Symbolic Processing. Author Index. Subject Index.
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