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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

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  • © 2013

Overview

Editors:
  1. David L. Dowe

    1. Faculty of Information Technology, Clayton School of Information Technology, Monash University, Clayton, Australia

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  • 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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Table of contents (35 chapters)

  1. Front Matter

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  2. Introduction

    1. Introduction to Ray Solomonoff 85th Memorial Conference

      • David L. Dowe
      Pages 1-36

  3. Invited Papers

    1. Ray Solomonoff and the New Probability

      • Grace Solomonoff
      Pages 37-52

    2. Universal Heuristics: How Do Humans Solve “Unsolvable” Problems?

      • Leonid A. Levin
      Pages 53-54

    3. Partial Match Distance

      • Ming Li
      Pages 55-64

  4. Long Papers

    1. Falsification and Future Performance

      • David Balduzzi
      Pages 65-78

    2. The Semimeasure Property of Algorithmic Probability – “Feature” or “Bug”?

      • Douglas Campbell
      Pages 79-90

    3. Inductive Inference and Partition Exchangeability in Classification

      • Jukka Corander, Yaqiong Cui, Timo Koski
      Pages 91-105

    4. Learning in the Limit: A Mutational and Adaptive Approach

      • Reginaldo Inojosa da Silva Filho, Ricardo Luis de Azevedo da Rocha, Ricardo Henrique Gracini Guiraldelli
      Pages 106-118

    5. Algorithmic Simplicity and Relevance

      • Jean-Louis Dessalles
      Pages 119-130

    6. Categorisation as Topographic Mapping between Uncorrelated Spaces

      • T. Mark Ellison
      Pages 131-141

    7. Algorithmic Information Theory and Computational Complexity

      • Rūsiņš Freivalds
      Pages 142-154

    8. A Critical Survey of Some Competing Accounts of Concrete Digital Computation

      • Nir Fresco
      Pages 155-173

    9. Further Reflections on the Timescale of AI

      • J. Storrs Hall
      Pages 174-183

    10. Towards Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL

      • Bing Hu, Thanawin Rakthanmanon, Yuan Hao, Scott Evans, Stefano Lonardi, Eamonn Keogh
      Pages 184-197

    11. Complexity Measures for Meta-learning and Their Optimality

      • Norbert Jankowski
      Pages 198-210

    12. Design of a Conscious Machine

      • P. Allen King
      Pages 211-222

    13. No Free Lunch versus Occam’s Razor in Supervised Learning

      • Tor Lattimore, Marcus Hutter
      Pages 223-235

    14. An Approximation of the Universal Intelligence Measure

      • Shane Legg, Joel Veness
      Pages 236-249

    15. Minimum Message Length Analysis of the Behrens–Fisher Problem

      • Enes Makalic, Daniel F. Schmidt
      Pages 250-260
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Keywords

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.

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: 978-3-642-44957-4Published: 11 November 2013

  • eBook ISBN: 978-3-642-44958-1Published: 22 October 2013

  • 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, general

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