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.
General
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