Neural Networks for Knowledge Representation and Inference

Abstract

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. Gunther, Automatic Versus Controlled Processing in Variable Temporal Context and Stimulus-Response Mapping.