Neural Networks for Knowledge Representation and Inference

Author:   Daniel S. Levine ,  Manuel Aparicio IV ,  Daniel S. Levine ,  Manuel Aparicio IV
Publisher:   Taylor & Francis Inc
ISBN:  

9780805811582


Pages:   528
Publication Date:   01 October 1993
Format:   Hardback
Availability:   In Print   Availability explained
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Neural Networks for Knowledge Representation and Inference


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Author:   Daniel S. Levine ,  Manuel Aparicio IV ,  Daniel S. Levine ,  Manuel Aparicio IV
Publisher:   Taylor & Francis Inc
Imprint:   Psychology Press
Dimensions:   Width: 15.20cm , Height: 3.80cm , Length: 22.90cm
Weight:   1.088kg
ISBN:  

9780805811582


ISBN 10:   0805811583
Pages:   528
Publication Date:   01 October 1993
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Contents: Preface. Part I: Neurons and Symbols: Toward a Reconciliation.M. Aparicio IV, D.S. Levine, Why Are Neural Networks Relevant to Higher Cognitive Function? J.A. Barnden, On Using Analogy to Reconcile Connections and Symbols. S.J. Leven, Semiotics, Meaning, and Discursive Neural Networks. B. MacLennan, Continuous Symbol Systems: The Logic of Connectionism. Part II: Architectures for Knowledge Representation.A. Jagota, Representing Discrete Structures in a Hopfield-Style Network. W.P. Mounfield, Jr., L. Grujic, S. Guddanti, Modeling and Stability Analysis of a Truth Maintenance System Neural Network. G. Pinkas, Propositional Logic, Nonmonotonic Reasoning, and Symmetric Networks -- On Bridging the Gap Between Symbolic and Connectionist Knowledge Representation. T. Jackson, J. Austin, The Representation of Knowledge and Rules in Hierarchical Neural Networks. Part III: Applications of Connectionist Representation.R. Sun, Connectionist Models of Commonsense Reasoning. W.R.P. Raghupathi, D.S. Levine, R.S. Bapi, L.L. Schkade, Toward Connectionist Representation of Legal Knowledge. R.M. Golden, D.M. Rumelhart, J. Strickland, A. Ting, Markov Random Fields for Text Comprehension. J.A. Anderson, K.T. Spoehr, D.J. Bennett, A Study in Numerical Perversity: Teaching Arithmetic to a Neural Network. Part IV: Biological Foundations of Knowledge.G.E. Mobus, Toward A Theory of Learning and Representing Causal Inferences in Neural Networks. K.H. Pribram, Brain and the Structure of Narrative. W.J. Hudspeth, Neuroelectric Eigenstructures of Mental Representation. J.P. Banquet, S. El Ouardirhi, A. Spinakis, M. Smith, W. Günther, Automatic Versus Controlled Processing in Variable Temporal Context and Stimulus-Response Mapping.

Reviews

"""Neural networkers will want to read this collection from cover to cover....wonderful, worthwhile collection."" —AI Expert ""...the opening chapter by Aparicio and Levine is a first-rate exposition of the historical roots of the connectionist movement and paradigmatic struggles taking place within traditional interdisciplinary fields of AI and cognitive science....a unique and fascinating collection of applications of neural networks for modeling everyday sorts of reasoning."" —Contemporary Psychology"


Neural networkers will want to read this collection from cover to cover....wonderful, worthwhile collection. -AI Expert ...the opening chapter by Aparicio and Levine is a first-rate exposition of the historical roots of the connectionist movement and paradigmatic struggles taking place within traditional interdisciplinary fields of AI and cognitive science....a unique and fascinating collection of applications of neural networks for modeling everyday sorts of reasoning. -Contemporary Psychology


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Daniel S. Levine, Manuel Aparicio IV

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