Neural Networks for Knowledge Representation and Inference

The second published collection based on a conference sponsored by the Metroplex Institute for Neural Dynamics -- the first is Motivation, Emotion, and Goal Direction in Neural Networks (LEA, 1992) -- this book addresses the controversy between symbolicist artificial intelligence and neural network theory. A particular issue is how well neural networks -- well established for statistical pattern matching -- can perform the higher cognitive functions that are more often associated with symbolic approaches. This controversy has a long history, but recently erupted with arguments against the abilities of renewed neural network developments. More broadly than other attempts, the diverse contributions presented here not only address the theory and implementation of artificial neural networks for higher cognitive functions, but also critique the history of assumed epistemologies -- both neural networks and AI -- and include several neurobiological studies of human cognition as a real system to guide the further development of artificial ones. Organized into four major sections, this volume: * outlines the history of the AI/neural network controversy, the strengths and weaknesses of both approaches, and shows the various capabilities such as generalization and discreetness as being along a broad but common continuum; * introduces several explicit, theoretical structures demonstrating the functional equivalences of neurocomputing with the staple objects of computer science and AI, such as sets and graphs; * shows variants on these types of networks that are applied in a variety of spheres, including reasoning from a geographic database, legal decision making, story comprehension, and performing arithmetic operations; * discusses knowledge representation process in living organisms, including evidence from experimental psychology, behavioral neurobiology, and electroencephalographic responses to sensory stimuli.

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.

Daniel S. Levine, Manuel Aparicio IV