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Neural-Symbolic Cognitive Reasoning

  • Textbook
  • © 2009

Overview

Authors:
  1. Artur S. d’Avila Garcez

    1. Department of Computing School of Informatics, City University London, London, UK

  2. Luís C. Lamb

    1. Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil

  3. Dov M. Gabbay

    1. Department of Computer Science, King’s College London, London, UK

Part of the book series: Cognitive Technologies (COGTECH)

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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.

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. Introduction

    Pages 1-7

  3. Logic and Knowledge Representation

    Pages 9-21

  4. Artificial Neural Networks

    Pages 23-33

  5. Neural-Symbolic Learning Systems

    Pages 35-54

  6. Connectionist Modal Logic

    Pages 55-74

  7. Connectionist Temporal Reasoning

    Pages 75-85

  8. Connectionist Intuitionistic Reasoning

    Pages 87-100

  9. Applications of Connectionist Nonclassical Reasoning

    Pages 101-113

  10. Fibring Neural Networks

    Pages 115-126

  11. Relational Learning in Neural Networks

    Pages 127-141

  12. Argumentation Frameworks as Neural Networks

    Pages 143-159

  13. Reasoning about Probabilities in Neural Networks

    Pages 161-167

  14. Conclusions

    Pages 169-180

  15. Back Matter

    Pages 181-197
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Authors and Affiliations

  • Department of Computing School of Informatics, City University London, London, UK

    Artur S. d’Avila Garcez

  • Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil

    Luís C. Lamb

  • Department of Computer Science, King’s College London, London, UK

    Dov M. Gabbay

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