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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

Editors:

  • 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 book series: Lecture Notes in Computer Science (LNCS, volume 7070)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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  • ISBN: 978-3-642-44958-1
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Table of contents (35 chapters)

  1. Long Papers

    1. MMLD Inference of Multilayer Perceptrons

      • Enes Makalic, Lloyd Allison
      Pages 261-272
    2. Diverse Consequences of Algorithmic Probability

      • Eray Ă–zkural
      Pages 285-298
    3. Toward an Algorithmic Metaphysics

      • Steve Petersen
      Pages 306-317
    4. Limiting Context by Using the Web to Minimize Conceptual Jump Size

      • Rafal Rzepka, Koichi Muramoto, Kenji Araki
      Pages 318-326
    5. On the Application of Algorithmic Probability to Autoregressive Models

      • Ray J. Solomonoff, Elias G. Saleeby
      Pages 366-385
    6. Principles of Solomonoff Induction and AIXI

      • Peter Sunehag, Marcus Hutter
      Pages 386-398
    7. (Non-)Equivalence of Universal Priors

      • Ian Wood, Peter Sunehag, Marcus Hutter
      Pages 417-425
    8. A Syntactic Approach to Prediction

      • John Woodward, Jerry Swan
      Pages 426-438
  2. Short Paper

    1. Developing Machine Intelligence within P2P Networks Using a Distributed Associative Memory

      • Amiza Amir, Anang Hudaya M. Amin, Asad Khan
      Pages 439-443
  3. Back Matter

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.

Keywords

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

Editors and Affiliations

  • Faculty of Information Technology, Clayton School of Information Technology, Monash University, Clayton, Australia

    David L. Dowe

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: https://doi.org/10.1007/978-3-642-44958-1

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2013

  • 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

  • Topics: Computer Science

  • Industry Sectors: Aerospace, Automotive, Electronics, IT & Software, Law, Pharma, Telecommunications

Buying options

eBook USD 59.99
Price excludes VAT (USA)
  • ISBN: 978-3-642-44958-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 74.99
Price excludes VAT (USA)