Neural-Symbolic Cognitive Reasoning
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| Verlag | Springer |
| Auflage | 2010 |
| Seiten | 198 |
| Format | 23,6 x 26,0 x 1,1 cm |
| Gewicht | 324 g |
| Artikeltyp | Englisches Buch |
| Reihe | Cognitive Technologies |
| ISBN-10 | 3642092292 |
| EAN | 9783642092299 |
| Bestell-Nr | 64209229A |
This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.
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 researc hers, 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.
Inhaltsverzeichnis:
Logic and Knowledge Representation.- Artificial Neural Networks.- Neural-Symbolic Learning Systems.- Connectionist Modal Logic.- Connectionist Temporal Reasoning.- Connectionist Intuitionistic Reasoning.- Applications of Connectionist Nonclassical Reasoning.- Fibring Neural Networks.- Relational Learning in Neural Networks.- Argumentation Frameworks as Neural Networks.- Reasoning about Probabilities in Neural Networks.- Conclusions.
