Neural-Symbolic Cognitive Reasoning

  • Artur S. d’Avila Garcez
  • Luís C. Lamb
  • Dov M. Gabbay

Part of the Cognitive Technologies book series (COGTECH)

About this book


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.


artificial intelligence intelligence knowledge representation learning logic machine learning modal logic probabilistic reasoning

Authors and affiliations

  • Artur S. d’Avila Garcez
    • 1
  • Luís C. Lamb
    • 2
  • Dov M. Gabbay
    • 3
  1. 1.Department of Computing School of InformaticsCity University LondonLondonUK
  2. 2.Institute of InformaticsFederal University of Rio Grande do SulPorto Alegre, RSBrazil
  3. 3.Department of Computer ScienceKing’s College LondonLondonUK

Bibliographic information

  • DOI
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-73245-7
  • Online ISBN 978-3-540-73246-4
  • Series Print ISSN 1611-2482
  • Buy this book on publisher's site
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