
Neural Networks for Knowledge Representation and Inference
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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.
Reviews / Votes
"Neural networkers will want to read this collection from cover to cover....wonderful, worthwhile collection."-AI Expert
"...the opening chapter by Aparicio and Levine is a first-rate exposition of the historical roots of the connectionist movement and paradigmatic struggles taking place within traditional interdisciplinary fields of AI and cognitive science....a unique and fascinating collection of applications of neural networks for modeling everyday sorts of reasoning."
-Contemporary Psychology
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