An Introduction to Universal Artificial Intelligence
eBook - ePub

An Introduction to Universal Artificial Intelligence

  1. 516 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

An Introduction to Universal Artificial Intelligence

About this book

An Introduction to Universal Artificial Intelligence provides the formal underpinning of what it means for an agent to act intelligently in an unknown environment. First presented in Universal Algorithmic Intelligence (Hutter, 2000), UAI offers a framework in which virtually all AI problems can be formulated, and a theory of how to solve them. UAI unifies ideas from sequential decision theory, Bayesian inference, and algorithmic information theory to construct AIXI, an optimal reinforcement learning agent that learns to act optimally in unknown environments. AIXI is the theoretical gold standard for intelligent behavior.

The book covers both the theoretical and practical aspects of UAI. Bayesian updating can be done efficiently with context tree weighting, and planning can be approximated by sampling with Monte Carlo tree search. It provides algorithms for the reader to implement, and experimental results to compare against. These algorithms are used to approximate AIXI. The book ends with a philosophical discussion of Artificial General Intelligence: Can super-intelligent agents even be constructed? Is it inevitable that they will be constructed, and what are the potential consequences?

This text is suitable for late undergraduate students. It provides an extensive chapter to fill in the required mathematics, probability, information, and computability theory background. You can also visit the author website: http://www.hutter1.net/ai/uaibook2.htm.

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Yes, you can access An Introduction to Universal Artificial Intelligence by Marcus Hutter,David Quarel,Elliot Catt in PDF and/or ePUB format, as well as other popular books in Computer Science & Computer Science General. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover Page
  2. Half-Title Page
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Endorsements Page
  7. Dedication Page
  8. Preface
  9. Table of Contents
  10. List of Figures and Tables
  11. List of Algorithms
  12. I Introduction & Background
  13. II Algorithmic Prediction
  14. III A Family of Universal Agents
  15. IV Approximating Universal Agents
  16. V Alternative Approaches
  17. VI Safety and Discussion
  18. Bibliography
  19. Table of Notation
  20. Index