Artificial intelligence and neural networks : steps toward principled integration / edited by Vasant Honavar, Leonard Uhr.

Format
Book
Language
English
Published/​Created
Boston : Academic Press, [1994], ©1994.
Description
xxxii, 653 pages : illustrations ; 24 cm.

Availability

Copies in the Library

Location Call Number Status Location Service Notes
ReCAP - Remote StorageQA76.87 .H66 1994 Browse related items Request

    Details

    Subject(s)
    Related name
    Series
    Neural networks, foundations to applications. [More in this series]
    Bibliographic references
    Includes bibliographical references and index.
    Contents
    • Ch. I. Horses of A Different Colour? / Margaret Boden
    • Ch. II. Architecture of Intelligence: The Problems and Current Approaches to Solutions / B. Chandrasekaran and Susan G. Josephson
    • Ch. III. Schema Theory: Cooperative Computation for Brain Theory and Distributed AI / Michael Arbib
    • Ch. IV. The Role of Interdisciplinary Research Involving Neuroscience in the Development of Intelligent Systems / Thomas M. McKenna
    • Ch. V. Why the Difference between Connectionism and Anything Else Is More Than You Might Think but Less Than You Might Hope / Gregg C. Oden
    • Ch. VI. Beyond Symbolic: Toward a Kama-Sutra of Compositionality / Timothy van Gelder and Robert Port
    • Ch. VII. How Might Connectionist Systems Represent Propositional Attitudes? / John A. Barnden
    • Ch. VIII. Three Horns of the Representational Trilemma / Noel A. Sharkey and Stuart A. Jackson
    • Ch. IX. Learned Categorical Perception in Neural Nets: Implications for Symbol Grounding / Steven Harnad, Stephen J. Hanson and Joseph Lubin.
    • Ch. X. Image and Symbol: Continuous Computation and the Emergence of the Discrete / Bruce J. MacLennan
    • Ch. XI. Graded State Machines: The Representation of Temporal Contingencies in Simple Recurrent Networks / David Servan-Schreiber, Axel Cleeremans and James L. McClelland
    • Ch. XII. Extraction and Insertion of Symbolic Information in Recurrent Neural Networks / Christian W. Omlin and C. L. Giles
    • Ch. XIII. Logics and Variables in Connectionist Models: A Brief Overview / Ron Sun
    • Ch. XIV. A Fault-Tolerant Connectionist Architecture for Construction of Logic Proofs / Gadi Pinkas
    • Ch. XV. Digital and Analog Microcircuit and Sub-Net Structures for Connectionist Networks / Leonard Uhr
    • Ch. XVI. Encoding Shape and Spatial Relations: A Simple Mechanism for Coordinating Complimentary Representations / Stephen M. Kosslyn and Robert A. Jacobs.
    • Ch. XVII. Integrating Symbolic and Neural Processing in a Self-Organizing Architecture for Pattern Recognition and Prediction / Gail A. Carpenter and Stephen Grossberg
    • Ch. XVIII. Connectionist Grammars for High-Level Vision / Eric Mjolsness
    • Ch. XIX. Grounding Language in Perception / Michael G. Dyer
    • Ch. XX. Integrated Connectionist Models: Building AI Systems on Subsymbolic Foundations / Risto Miikkulainen
    • Ch. XXI. Integrating Connectionist and Symbolic Computation for the Theory of Language / Paul Smolensky, Geraldine Legendre and Yoshiro Miyata
    • Ch. XXII. The Unified Learning Paradigm: A Foundation for AI / Lev Goldfarb and Sandeep Nigam
    • Ch. XXIII. A Framework for Combining Symbolic and Neural Learning / Jude W. Shavlik
    • Ch. XXIV. Learning and Representation in Classifier Systems / Lashon B. Booker, Rick L. Riolo and John H. Holland
    • Ch. XXV. Toward Learning Systems That Integrate Different Strategies and Representations / Vasant Honavar.
    ISBN
    0123550556 (acid-free paper)
    LCCN
    93049702
    OCLC
    29594629
    RCP
    C - S
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