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.
- Book Title Neural-Symbolic Cognitive Reasoning
- Series Title Cognitive Technologies
- DOI https://doi.org/10.1007/978-3-540-73246-4
- Copyright Information Springer Berlin Heidelberg 2009
- Publisher Name Springer, Berlin, Heidelberg
- eBook Packages Computer Science Computer Science (R0)
- Hardcover ISBN 978-3-540-73245-7
- Softcover ISBN 978-3-642-09229-9
- eBook ISBN 978-3-540-73246-4
- Series ISSN 1611-2482
- Edition Number 1
- Number of Pages XIV, 198
- Number of Illustrations 53 b/w illustrations, 0 illustrations in colour
Computation by Abstract Devices
Theory of Computation
Mathematical Logic and Formal Languages
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