Neural Networks for Knowledge Representation and Inference

Aparicio IV, Manuel, Levine, Daniel S.

ISBN 10: 0805811591 / ISBN 13: 9780805811599

Edité par Psychology Press, 1993

A propos de cet article

522 pp., paperback, FAINT wear to cover edges else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

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Détails bibliographiques

• Titre: Neural Networks for Knowledge Representation and Inference
• Éditeur: Psychology Press
• Date de parution: 1993
• Langue: anglais
• ISBN à 10 chiffres: 0805811591
• ISBN à 13 chiffres: 9780805811599
• Reliure: Couverture souple
• État de l'article: Fine

A propos de ce titre

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.

À propos de l?auteur

Daniel S. Levine, Manuel Aparicio IV