Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: Cognitive Technologies (COGTECH)
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Table of contents (13 chapters)
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Front Matter
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Back Matter
Keywords
About this book
Humans are often extraordinary at performing practical reasoning. There are cases where the human computer, slow as it is, is faster than any artificial intelligence system. Are we faster because of the way we perceive knowledge as opposed to the way we represent it?
The authors address this question by presenting neural network models that integrate the two most fundamental phenomena of cognition: our ability to learn from experience, and our ability to reason from what has been learned. This book is the first to offer a self-contained presentation of neural network models for a number of computer science logics, including modal, temporal, and epistemic logics. By using a graphical presentation, it explains neural networks through a sound neural-symbolic integration methodology, and it focuses on the benefits of integrating effective robust learning with expressive reasoning capabilities.
The book will be invaluable reading for academic researchers, graduate students, and senior undergraduates in computer science, artificial intelligence, machine learning, cognitive science and engineering. It will also be of interest to computational logicians, and professional specialists on applications of cognitive, hybrid and artificial intelligence systems.
Authors and Affiliations
Bibliographic Information
Book Title: Neural-Symbolic Cognitive Reasoning
Authors: Artur S. d’Avila Garcez, Luís C. Lamb, Dov M. Gabbay
Series Title: Cognitive Technologies
DOI: https://doi.org/10.1007/978-3-540-73246-4
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2009
Hardcover ISBN: 978-3-540-73245-7Published: 22 October 2008
Softcover ISBN: 978-3-642-09229-9Published: 18 November 2010
eBook ISBN: 978-3-540-73246-4Published: 15 October 2008
Series ISSN: 1611-2482
Series E-ISSN: 2197-6635
Edition Number: 1
Number of Pages: XIV, 198
Number of Illustrations: 53 b/w illustrations
Topics: Artificial Intelligence, Computation by Abstract Devices, Theory of Computation, Logic, Mathematical Logic and Formal Languages, Pattern Recognition