© 2005

Universal Artificial Intelligence

Sequential Decisions Based on Algorithmic Probability


Table of contents

  1. Front Matter
    Pages i-xx
  2. Marcus Hutter
    Pages 1-27
  3. Marcus Hutter
    Pages 29-63
  4. Marcus Hutter
    Pages 65-124
  5. Marcus Hutter
    Pages 125-140
  6. Marcus Hutter
    Pages 141-183
  7. Marcus Hutter
    Pages 185-208
  8. Marcus Hutter
    Pages 209-229
  9. Marcus Hutter
    Pages 231-249
  10. Back Matter
    Pages 251-280

About this book


Decision Theory = Probability + Utility Theory
              +                                             +

Universal Induction = Ockham + Bayes + Turing
              =                                     =
A Unified View of Artificial Intelligence

This book presents sequential decision theory from a novel algorithmic information theory perspective. While the former is suited for active agents in known environments, the latter is suited for passive prediction in unknown environments.

The book introduces these two well-known but very different ideas and removes the limitations by unifying them to one parameter-free theory of an optimal reinforcement learning agent embedded in an arbitrary unknown environment. Most if not all AI problems can easily be formulated within this theory, which reduces the conceptual problems to pure computational ones. Considered problem classes include sequence prediction, strategic games, function minimization, reinforcement and supervised learning. The discussion includes formal definitions of intelligence order relations, the horizon problem and relations to other approaches to AI. One intention of this book is to excite a broader AI audience about abstract algorithmic information theory concepts, and conversely to inform theorists about exciting applications to AI.


agents algorithmic information theory algorithms artificial intelligence decision theory information information theory intelligence learning reinforcement learning uncertainty

Authors and affiliations

  1. 1.Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA)Manno-LuganoSwitzerland

About the authors

Marcus Hutter received his masters in computer sciences in 1992 at the Technical University in Munich, Germany. After his PhD in theoretical particle physics he developed algorithms in a medical software company for 5 years. For four years he has been working as a researcher at the AI institute IDSIA in Lugano, Switzerland. His current interests are centered around reinforcement learning, algorithmic information theory and statistics, universal induction schemes, adaptive control theory, and related areas.

IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale) is a non-profit oriented research institute for artificial intelligence, affiliated with both the University of Lugano and SUPSI. It focusses on machine learning (artificial neural networks, reinforcement learning), optimal universal artificial intelligence and optimal rational agents, operations research, complexity theory, and robotics. In Business Week's "X-Lab Survey" IDSIA was ranked in fourth place in the category "Computer Science - Biologically Inspired", after much larger institutions. IDSIA also ranked in the top 10 of the broader category "Artificial Intelligence."

Bibliographic information

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