New York: Springer (2009)

Authors
Dov Gabbay
Hebrew University of Jerusalem
Luis Lamb
Universidade Federal do Rio Grande do Sul
Abstract
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.
Keywords Cognitive Reasoning  Machine Learning  Learning and Reasoning  Neural Reasoning  Machine Reasoning  Neural-Symbolic Computation
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Illustrating a Neural Model of Logic Computations: The Case of Sherlock Holmes’ Old Maxim.Eduardo Mizraji - 2016 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 31 (1):7-25.

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