Artificial Intelligence and Neural Networks:

Steps Toward Principled Integration

Vasant Honavar and Leonard Uhr (Ed.)


Boston: Academic Press (1994).


ISBN 0-12-355055-6, 653+xxxii pages


Currently marketed by Elsevier and can be ordered from Barnes and Noble or Amazon.

Summary

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.

Table of Contents

• Preface
• Introduction: Artificial Intelligence and Neural Networks: Steps Toward Principled Integration. Leonard Uhr & Vasant Honavar
• Chapter I: Horses of a Different Color? Margaret Boden
• Chapter II: Architecture of Intelligence: The Problems and Current Approaches to Solutions. B. Chandrasekaran & Susan G. Josephson
• Chapter III: Schema Theory: Cooperative Computation for Brain Theory and Distributed AI. Michael Arbib
• Chapter IV: The Role of Inter-disciplinary Research Involving Neuroscience in the Development of Intelligent Systems. Thomas M. McKenna
• Chapter V: Why the Difference Between Connectionism and Anything Else is More Than You Might Think but Less Than You Might Hope. Gregg C. Oden
• Chapter VI: Beyond Symbolic: Prolegomena to a Kama-Sutra of Compositionality. Timothy van Gelder & Robert Port
• Chapter VII: How Might Connectionist Systems Represent Propositional Attitudes? John A. Barnden
• Chapter VIII: Three Horns of the Representational Trilemma. Noel A. Sharkey & Stuart A. Jackson
• Chapter IX: Learned Categorical Perception in Neural nets: Implications for Symbol Grounding. Steven Harnad, Stephen J. Hanson & Joseph Lubin
• Chapter X: Image and Symbol: Continuous Computation and the Emergence of The Discrete. Bruce J. MacLennan
• Chapter XI: Graded State Machines: The Representation of Temporal Contingencies in Simple Recurrent Networks.David Servan-Schreiber, Axel Cleermans & James L. McClelland
• Chapter XII: Extraction and Insertion of Symbolic Information in Recurrent Neural Networks. Christian W. Omlin & C. L. Giles
• Chapter XIII: Logics and Variables in Connectionist Models: A Brief Overview. Ron Sun
• Chapter XIV: A Fault-Tolerant Connectionist Architecture for Construction of Logic Proofs. Gadi Pinkas
• Chapter XV: Digital and Analog Micro-circuit and Sub-net Structures for Connectionist Networks. Leonard Uhr
• Chapter XVI: Encoding Shape and Spatial Relations: A Simple Mechanism for Coordinating Complementary Representations. Stephen M. Kosslyn & Robert A. Jacobs
• Chapter XVII: Integrating Symbolic and Neural Processing in a Self-Organizing Architecture for Pattern Recognition and Prediction. Gail A. Carpenter & Stephen Grossberg
• Chapter XVIII: Connectionist Grammars For High-Level Vision. Eric Mjolsness
• Chapter XIX: Grounding Language in Perception. Michael G. Dyer
• Chapter XX: Integrated Connectionist Models: Building AI Systems on Sub-symbolic Foundations. Risto Miikkulainen
• Chapter XXI: Integrating Connectionist and Symbolic Computation for the Theory of Language. Paul Smolensky, Geraldine Legendre & Yoshiro Miyata
• Chapter XXII: Unified Learning Paradigm: A Foundation for AI. Lev Goldfarb & Sandeep Nigam
• Chapter XXIII: A Framework for Combining Symbolic and Subsymbolic Learning. Jude W. Shavlik
• Chapter XXIV: Learning and Representation in Classifier Systems. Lashon B. Booker, Rick L. Riolo & J. H. Holland
• Chapter XXV: Toward Learning Systems That Integrate Different Strategies and Representations. Vasant Honavar
• Index