Dr. Artur S. d’Avila Garcez, Dr. Luís C. Lamb, Prof. Dov M. Gabbay (auth.)9783642092299, 3642092292
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.
Table of contents :
Front Matter….Pages i-xiii
Introduction….Pages 1-7
Logic and Knowledge Representation….Pages 9-21
Artificial Neural Networks….Pages 23-33
Neural-Symbolic Learning Systems….Pages 35-54
Connectionist Modal Logic….Pages 55-74
Connectionist Temporal Reasoning….Pages 75-85
Connectionist Intuitionistic Reasoning….Pages 87-100
Applications of Connectionist Nonclassical Reasoning….Pages 101-113
Fibring Neural Networks….Pages 115-126
Relational Learning in Neural Networks….Pages 127-141
Argumentation Frameworks as Neural Networks….Pages 143-159
Reasoning about Probabilities in Neural Networks….Pages 161-167
Conclusions….Pages 169-180
Back Matter….Pages 181-197
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