Neural Networks for Knowledge Representation and Inference

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  • Edition: 1st
  • Format: Hardcover
  • Copyright: 1993-10-01
  • Publisher: Psychology Pres

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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.

Table of Contents

Prefacep. ix
References for Prefacep. xi
List of Contributorsp. xiii
Neurons and Symbols: toward a Reconciliationp. xvii
Why Are Neural Networks Relevant to Higher Cognitive Function?p. 1
On Using Analogy to Reconcile Connections and Symbolsp. 27
Semiotics, Meaning, and Discursive Neural Networksp. 65
Continuous Symbol Systems: the Logic of Connectionismp. 83
Architectures for Knowledge Representationp. 121
Representing Discrete Structures in a Hopfield-Style Networkp. 123
Modeling and Stability Analysis of a Truth Maintenance System Neural Networkp. 143
Propositional Logic, Nonmonotonic Reasoning and Symmetric Networks -- on Bridging the Gap between Symbolic and Connectionist Knowledge Representationp. 175
The Representation of Knowledge and Rules in Hierarchical Neural Networksp. 205
Applications of Connectionist Representationp. 239
Connectionist Models of Commonsense Reasoningp. 241
The Collins Protocolsp. 263
Toward Connectionist Representation of Legal Knowledgep. 269
Markov Random Fields for Text Comprehensionp. 283
A Study in Numerical Perversity: Teaching Arithmetic to a Neural Networkp. 311
Biological Foundations of Knowledgep. 337
Toward a Theory of Learning and Representing Causal Inferences in Neural Networksp. 339
Brain and the Structure of Narrativep. 375
Neuroelectric Eigenstructures of Mental Representationp. 419
Automatic versus Controlled Processing in Variable Temporal Context and Stimulus-Response Mappingp. 447
Author Indexp. 471
Subject Indexp. 483
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