Neural-Symbolic Cognitive Reasoning by Dov M. Gabbay (English) Hardcover Book

Description: Neural-Symbolic Cognitive Reasoning by Dov M. Gabbay, Artur S. D'Avila Garcez, Luís C. Lamb 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. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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. Notes 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 using a sound, neural-symbolic integration methodology, and it focuses on the benefits of integrating effective robust learning with expressive reasoning capability. Table of Contents 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. Long Description 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. Feature The first book to offer a self-contained presentation of neural network models for a number of computer science logics, including modal, temporal, and epistemic logics Description for Sales People 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 using a sound, neural-symbolic integration methodology, and it focuses on the benefits of integrating effective robust learning with expressive reasoning capability. Details ISBN3540732454 Author Luís C. Lamb Short Title NEURAL-SYMBOLIC COGNITIVE REAS Series Cognitive Technologies Language English ISBN-10 3540732454 ISBN-13 9783540732457 Media Book Format Hardcover Year 2008 Imprint Springer-Verlag Berlin and Heidelberg GmbH & Co. K Place of Publication Berlin Country of Publication Germany DEWEY 006.32 Birth 1945 Affiliation Kings College London Pages 198 Publisher Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Publication Date 2008-10-22 Alternative 9783642092299 Illustrations 53 Illustrations, black and white; XIV, 198 p. 53 illus. Audience Undergraduate Edition Description 2009 ed. Edition 2009th We've got this At The Nile, if you're looking for it, we've got it. 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