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Neural-Symbolic Cognitive Reasoning

By Artur S. D'Avila Garcez , Luís C. Lamb , Dov M. Gabbay

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This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.

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  • ISBN13: 978-3-5407-3245-7
  • 212 Pages
  • User Level: Students
  • Publication Date: October 15, 2008
  • Available eBook Formats: PDF
Full 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.
Table of Contents

Table of Contents

  1. Introduction.
  2. Logics and Knowledge Representation.
  3. Artificial Neural Networks.
  4. Neural
  5. Symbolic Learning Systems.
  6. Connectionist Modal Logic.
  7. Applications of Connectionist Non
  8. classical Reasoning.
  9. Connectionist Modal Logics in Practice.
  10. Connectionist Temporal Logic.
  11. Connectionist Intuitionistic Logic.
  12. Fibring Neural Networks.
  13. Argumentation Frameworks as Neural Networks.
  14. Probabilistic Reasoning in Neural Networks.
  15. Relational Learning in Neural Networks.
  16. Conclusions.

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