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.
By:
Artur S. D'Avila Garcez, Luís C. Lamb, Dov M. Gabbay Imprint: Springer-Verlag Berlin and Heidelberg GmbH & Co. K Country of Publication: Germany Edition: 2009 ed. Dimensions:
Height: 235mm,
Width: 155mm,
Spine: 12mm
Weight: 489g ISBN:9783540732457 ISBN 10: 3540732454 Series:Cognitive Technologies Pages: 198 Publication Date:22 October 2008 Audience:
College/higher education
,
Professional and scholarly
,
Professional & Vocational
,
A / AS level
,
Further / Higher Education
Format:Hardback Publisher's Status: Active