Neural Networks for Knowledge Representation and Inference

Daniel S. Levine

ISBN 10: 0805811583 / ISBN 13: 9780805811582

Published by Taylor & Francis Inc Okt 1993, 1993

About this Item

Neuware - This text addresses the symbolicist artificial intelligence and neural network theory. It explores the issue that neural networks can perform higher cognitive functions often associated with symbolic approaches, as well as outlining the history and theoretical structures of the controversy.

Seller Inventory # 9780805811582

Bibliographic Details

• Title: Neural Networks for Knowledge Representation and Inference
• Publisher: Taylor & Francis Inc Okt 1993
• Publication Date: 1993
• Language: English
• ISBN 10: 0805811583
• ISBN 13: 9780805811582
• Binding: Buch
• Condition: Neu
• Dimensions: 229W x 152H x 0D millimeters 229 millimeters width by 152 millimeters height by 0 millimeters depth
• Weight: 1293 grams

About this title

Synopsis

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.

About the Author

Daniel S. Levine, Manuel Aparicio IV