© 2013

Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence

Papers from the Ray Solomonoff 85th Memorial Conference, Melbourne, VIC, Australia, November 30 – December 2, 2011

  • David L. Dowe
  • Dedicated to one of the pioneers in computer science, artificial intelligence and machine learning

  • Usage of (universal) Turing machines for prediction problems in statistics, machine learning, econometrics and data mining

  • Covers a vast variety of topics such as statistics, econometrics and knowledge discovery, data mining, terabyte science, data science, big data and data management and processing


Part of the Lecture Notes in Computer Science book series (LNCS, volume 7070)

Table of contents

  1. Front Matter
  2. Introduction

  3. Invited Papers

  4. Long Papers

    1. David Balduzzi
      Pages 65-78
    2. Jukka Corander, Yaqiong Cui, Timo Koski
      Pages 91-105
    3. Reginaldo Inojosa da Silva Filho, Ricardo Luis de Azevedo da Rocha, Ricardo Henrique Gracini Guiraldelli
      Pages 106-118
    4. Jean-Louis Dessalles
      Pages 119-130
    5. J. Storrs Hall
      Pages 174-183
    6. Bing Hu, Thanawin Rakthanmanon, Yuan Hao, Scott Evans, Stefano Lonardi, Eamonn Keogh
      Pages 184-197
    7. P. Allen King
      Pages 211-222
    8. Tor Lattimore, Marcus Hutter
      Pages 223-235
    9. Shane Legg, Joel Veness
      Pages 236-249
    10. Enes Makalic, Daniel F. Schmidt
      Pages 250-260

About this book


Algorithmic probability and friends: Proceedings of the Ray Solomonoff 85th memorial conference is a collection of original work and surveys. The Solomonoff 85th memorial conference was held at Monash University's Clayton campus in Melbourne, Australia as a tribute to pioneer, Ray Solomonoff (1926-2009), honouring his various pioneering works - most particularly, his revolutionary insight in the early 1960s that the universality of Universal Turing Machines (UTMs) could be used for universal Bayesian prediction and artificial intelligence (machine learning). This work continues to increasingly influence and under-pin statistics, econometrics, machine learning, data mining, inductive inference, search algorithms, data compression, theories of (general) intelligence and philosophy of science - and applications of these areas. Ray not only envisioned this as the path to genuine artificial intelligence, but also, still in the 1960s, anticipated stages of progress in machine intelligence which would ultimately lead to machines surpassing human intelligence. Ray warned of the need to anticipate and discuss the potential consequences - and dangers - sooner rather than later. Possibly foremostly, Ray Solomonoff was a fine, happy, frugal and adventurous human being of gentle resolve who managed to fund himself while electing to conduct so much of his paradigm-changing research outside of the university system. The volume contains 35 papers pertaining to the abovementioned topics in tribute to Ray Solomonoff and his legacy.


Bayesian prediction algorithmic information theory algorithmic probability technological singularity universal turing machines

Editors and affiliations

  • David L. Dowe
    • 1
  1. 1.Faculty of Information Technology, Clayton School of Information TechnologyMonash UniversityClaytonAustralia

Bibliographic information

  • Book Title Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence
  • Book Subtitle Papers from the Ray Solomonoff 85th Memorial Conference, Melbourne, VIC, Australia, November 30 – December 2, 2011
  • Editors David L. Dowe
  • Series Title Lecture Notes in Computer Science
  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science Computer Science (R0)
  • Softcover ISBN 978-3-642-44957-4
  • eBook ISBN 978-3-642-44958-1
  • Series ISSN 0302-9743
  • Series E-ISSN 1611-3349
  • Edition Number 1
  • Number of Pages XVI, 445
  • Number of Illustrations 61 b/w illustrations, 0 illustrations in colour
  • Topics Computer Science, general
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