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:

ISBN: 978-3-642-44957-4 (Print) 978-3-642-44958-1 (Online)

Table of contents (35 chapters)

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  1. Front Matter

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  2. Introduction

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      Pages 1-36

      Introduction to Ray Solomonoff 85th Memorial Conference

  3. Invited Papers

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      Pages 37-52

      Ray Solomonoff and the New Probability

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      Pages 53-54

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

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      Pages 55-64

      Partial Match Distance

  4. Long Papers

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      Pages 65-78

      Falsification and Future Performance

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      Pages 79-90

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

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      Pages 91-105

      Inductive Inference and Partition Exchangeability in Classification

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      Pages 106-118

      Learning in the Limit: A Mutational and Adaptive Approach

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      Pages 119-130

      Algorithmic Simplicity and Relevance

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      Pages 131-141

      Categorisation as Topographic Mapping between Uncorrelated Spaces

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      Pages 142-154

      Algorithmic Information Theory and Computational Complexity

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      Pages 155-173

      A Critical Survey of Some Competing Accounts of Concrete Digital Computation

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      Pages 174-183

      Further Reflections on the Timescale of AI

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      Pages 184-197

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

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      Pages 198-210

      Complexity Measures for Meta-learning and Their Optimality

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      Pages 211-222

      Design of a Conscious Machine

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      Pages 223-235

      No Free Lunch versus Occam’s Razor in Supervised Learning

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      Pages 236-249

      An Approximation of the Universal Intelligence Measure

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      Pages 250-260

      Minimum Message Length Analysis of the Behrens–Fisher Problem

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      Pages 261-272

      MMLD Inference of Multilayer Perceptrons

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