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 / edited by David L. Dowe.

Format:

Book

Contributor:

Dowe, David L., editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 7070.

Lecture Notes in Artificial Intelligence ; 7070

Language:

English

Subjects (All):

Computer science.

Computer Science, general.

Local Subjects:

Computer Science, general.

Physical Description:

1 online resource (XVI, 445 pages) : 61 illustrations.

Edition:

First edition 2013.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.

System Details:

text file PDF

Summary:

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.

Contents:

Introduction to Ray Solomonoff 85th Memorial Conference

Ray Solomonoff and the New Probability

Universal Heuristics: How Do Humans Solve "Unsolvable" Problems?

Partial Match Distance

Falsification and Future Performance

The Semimeasure Property of Algorithmic Probability - "Feature" or "Bug"?

Inductive Inference and Partition Exchangeability in Classification

Learning in the Limit: A Mutational and Adaptive Approach

Algorithmic Simplicity and Relevance

Categorisation as Topographic Mapping between Uncorrelated Spaces

Algorithmic Information Theory and Computational Complexity

A Critical Survey of Some Competing Accounts of Concrete Digital Computation

Further Reflections on the Timescale of AI

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

Complexity Measures for Meta-learning and Their Optimality

Design of a Conscious Machine

No Free Lunch versus Occam's Razor in Supervised Learning

An Approximation of the Universal Intelligence Measure

Minimum Message Length Analysis of the Behrens-Fisher Problem

MMLD Inference of Multilayer Perceptrons

An Optimal Superfarthingale and Its Convergence over a Computable Topological Space

Diverse Consequences of Algorithmic Probability

An Adaptive Compression Algorithm in a Deterministic World

Toward an Algorithmic Metaphysics

Limiting Context by Using the Web to Minimize Conceptual Jump Size

Minimum Message Length Order Selection and Parameter Estimation of Moving Average Models

Abstraction Super-Structuring Normal Forms: Towards a Theory of Structural Induction

Locating a Discontinuity in a Piecewise-Smooth Periodic Function Using Bayes Estimation

On the Application of Algorithmic Probability to Autoregressive Models

Principles of Solomonoff Induction and AIXI

MDL/Bayesian Criteria Based on Universal Coding/Measure

Algorithmic Analogies to Kamae-Weiss Theorem on Normal Numbers

(Non-)Equivalence of Universal Priors

A Syntactic Approach to Prediction

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

Other Format:

Printed edition:

ISBN:

978-3-642-44958-1

9783642449581

Access Restriction:

Restricted for use by site license.