Neural Networks for Knowledge Representation and Inference / Edition 1

Neural Networks for Knowledge Representation and Inference / Edition 1

by Daniel S. Levine
     
 

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

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Overview

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.

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Product Details

ISBN-13:
9780805811582
Publisher:
Taylor & Francis
Publication date:
10/01/1993
Pages:
522
Product dimensions:
7.20(w) x 9.90(h) x 1.30(d)
Lexile:
1370L (what's this?)

Table of Contents

Preface
List of Contributors
1Why are Neural Networks Relevant to Higher Cognitive Function?1
2On Using Analogy to Reconcile Connections and Symbols27
3Semiotics, Meaning, and Discursive Neural Networks65
4Continuous Symbol Systems: The Logic of Connectionism83
5Representing Discrete Structures in a Hopfield-Style Network123
6Modeling and Stability Analysis of a Truth Maintenance System Neural Network143
7Propositional Logic, Nonmonotonic Reasoning, and Symmetric Networks - On Bridging the Gap Between Symbolic and Connectionist Knowledge Representation175
8The Representation of Knowledge and Rules in Hierarchical Neural Networks205
9Connectionist Models of Commonsense Reasoning241
10Toward Connectionist Representation of Legal Knowledge269
11Markov Random Fields for Text Comprehension283
12A Study in Numerical Perversity: Teaching Arithmetic to a Neural Network311
13Toward a Theory of Learning and Representing Causal Inferences in Neural Networks339
14Brain and the Structure of Narrative375
15Neuroelectric Eigenstructures of Mental Representation419
16Automatic Versus Controlled Processing in Variable Temporal Context and Stimulus-Response Mapping447
Author Index471
Subject Index483

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