Special Issue ""Algorithmic Complexity in Physics & Embedded Artificial Intelligences"—In Memoriam Ray Solomonoff (1926-2009)"

Quicklinks

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 August 2010)

Special Issue Editor

Guest Editor
Prof. Dr. Juergen Schmidhuber

IDSIA, Galleria 2, 6928 Manno-Lugano, Switzerland
Website | E-Mail
Interests: artificial intelligence; machine learning; neural networks; Kolmogorow-complexity; robotics

Special Issue Information

Dear Colleagues,

Is the universe computable, as suggested in the 1940s by Konrad Zuse, inventor of the first working program-controlled computer? With the ascent of virtual realities the idea has become popular, and is now also being taken seriously by physicists, for lack of contrarian physical evidence. Questions to be addressed in this special issue include: Which kind of programs running on which type of computational device could in principle provide a concise description of quantum physics? How can algorithmic complexity theory and Kolmogorov complexity theory guide the quest for simple explanations of the world in the sense of Occam's razor? How do Gödelian limits of mathematics and computation as well as insights from algorithmic information theory restrict the set of valid physical theories, including many world theories? Which sets of computable probability distributions or measures on possible universe histories make sense at all from the perspective of constructive mathematics? Following Solomonoff's theory of optimal inductive inference and algorithmic probability, how can the restrictions embodied by such sets help to predict future events, given past observations in a particular universe? Which testable predictions are made by algorithmic complexity-based theories of physics? Can we in principle design rational decision-making agents or artificial intelligences embedded in computable physics such that their decisions are optimal in reasonable mathematical senses? Which are the fundamental limitations of such decision makers? If physics is hard to compute, can this help to improve cryptography?

Special Issue "In Memoriam Ray Solomonoff" (1926-2009):

The Great Ray Solomonoff, pioneer of Machine Learning, founder of Algorithmic Probability Theory, father of the Universal Probability Distribution, creator of the Universal Theory of Inductive Inference, passed away on Monday 7 December 2009 at age 83. Ray Solomonoff was the first to describe the fundamental concept of Algorithmic Information or Kolmogorov Complexity. In the new millennium his work became the foundation of the first mathematical theory of Optimal Universal Artificial Intelligence. With great sadness the special issue will be "In Memoriam Ray Solomonoff".

Prof. Dr. Juergen Schmidhuber
Guest Editor

Keywords

  • algorithmic complexity
  • algorithmic information theory
  • Kolmogorov complexity
  • descriptive complexity
  • Kolmogorov-Chaitin complexity
  • stochastic complexity
  • algorithmic entropy
  • program-size complexity
  • Chaitin entropy
  • Chaitin complexity

Published Papers (4 papers)

View options order results:
result details:
Displaying articles 1-4
Export citation of selected articles as:

Research

Jump to: Other

Open AccessArticle A Complete Theory of Everything (Will Be Subjective)
Algorithms 2010, 3(4), 329-350; doi:10.3390/a3040329
Received: 30 August 2010 / Accepted: 22 September 2010 / Published: 29 September 2010
Cited by 9 | PDF Full-text (303 KB) | HTML Full-text | XML Full-text
Abstract
Increasingly encompassing models have been suggested for our world. Theories range from generally accepted to increasingly speculative to apparently bogus. The progression of theories from ego- to geo- to helio-centric models to universe and multiverse theories and beyond was accompanied by a dramatic
[...] Read more.
Increasingly encompassing models have been suggested for our world. Theories range from generally accepted to increasingly speculative to apparently bogus. The progression of theories from ego- to geo- to helio-centric models to universe and multiverse theories and beyond was accompanied by a dramatic increase in the sizes of the postulated worlds, with humans being expelled from their center to ever more remote and random locations. Rather than leading to a true theory of everything, this trend faces a turning point after which the predictive power of such theories decreases (actually to zero). Incorporating the location and other capacities of the observer into such theories avoids this problem and allows to distinguish meaningful from predictively meaningless theories. This also leads to a truly complete theory of everything consisting of a (conventional objective) theory of everything plus a (novel subjective) observer process. The observer localization is neither based on the controversial anthropic principle, nor has it anything to do with the quantum-mechanical observation process. The suggested principle is extended to more practical (partial, approximate, probabilistic, parametric) world models (rather than theories of everything). Finally, I provide a justification of Ockham’s razor, and criticize the anthropic principle, the doomsday argument, the no free lunch theorem, and the falsifiability dogma. Full article
Figures

Open AccessArticle Open Problems in Universal Induction & Intelligence
Algorithms 2009, 2(3), 879-906; doi:10.3390/a2030879
Received: 8 April 2009 / Revised: 15 June 2009 / Accepted: 16 June 2009 / Published: 2 July 2009
Cited by 9 | PDF Full-text (255 KB) | HTML Full-text | XML Full-text
Abstract
Specialized intelligent systems can be found everywhere: finger print, handwriting, speech, and face recognition, spam filtering, chess and other game programs, robots, et al. This decade the first presumably complete mathematical theory of artificial intelligence based on universal induction-prediction-decision-action has been proposed. This
[...] Read more.
Specialized intelligent systems can be found everywhere: finger print, handwriting, speech, and face recognition, spam filtering, chess and other game programs, robots, et al. This decade the first presumably complete mathematical theory of artificial intelligence based on universal induction-prediction-decision-action has been proposed. This informationtheoretic approach solidifies the foundations of inductive inference and artificial intelligence. Getting the foundations right usually marks a significant progress and maturing of a field. The theory provides a gold standard and guidance for researchers working on intelligent algorithms. The roots of universal induction have been laid exactly half-a-century ago and the roots of universal intelligence exactly one decade ago. So it is timely to take stock of what has been achieved and what remains to be done. Since there are already good recent surveys, I describe the state-of-the-art only in passing and refer the reader to the literature. This article concentrates on the open problems in universal induction and its extension to universal intelligence. Full article
Figures

Other

Jump to: Research

Open AccessObituary Ray Solomonoff (1926-2009)
Algorithms 2010, 3(3), 255-259; doi:10.3390/a30302555
Received: 12 March 2010 / Accepted: 14 March 2010 / Published: 20 July 2010
PDF Full-text (84 KB) | HTML Full-text | XML Full-text
Abstract
Ray Solomonoff was always inventive. As a child, he had a lab in his parent's cellar in Cleveland and a secret air hole to vent the smoke from his experiments. He gave his friend Marvin Minsky a so-called "Hurry" clock — a clock
[...] Read more.
Ray Solomonoff was always inventive. As a child, he had a lab in his parent's cellar in Cleveland and a secret air hole to vent the smoke from his experiments. He gave his friend Marvin Minsky a so-called "Hurry" clock — a clock labeled "HURRY" that ran very fast. Helped by a friend, he built a year round house in N.H. He put in thick insulation, enabling him to heat the house with two rows of light bulbs along the ceiling. I met Ray shortly after he finished this house, in 1969. I knew about foraging, so I showed him edible plants like Indian Cucumber Root. He was so happy: it was as if we found a fountain of champagne. [...] Full article
Open AccessObituary Ray Solomonoff, Founding Father of Algorithmic Information Theory
Algorithms 2010, 3(3), 260-264; doi:10.3390/a3030260
Received: 12 March 2010 / Accepted: 14 March 2010 / Published: 20 July 2010
PDF Full-text (68 KB) | HTML Full-text | XML Full-text
Abstract
Ray J. Solomonoff died on December 7, 2009, in Cambridge, Massachusetts, of complications of a stroke caused by an aneurism in his head. Ray was the first inventor of Algorithmic Information Theory which deals with the shortest effective description length of objects and
[...] Read more.
Ray J. Solomonoff died on December 7, 2009, in Cambridge, Massachusetts, of complications of a stroke caused by an aneurism in his head. Ray was the first inventor of Algorithmic Information Theory which deals with the shortest effective description length of objects and is commonly designated by the term “Kolmogorov complexity.” In the 1950s Solomonoff was one of the first researchers to treat probabilistic grammars and the associated languages. He treated probabilistic Artificial Intelligence (AI) when “probabilistic” was unfashionable, and treated questions of machine learning early on. But his greatest contribution is the creation of Algorithmic Information Theory. [...] Full article

Journal Contact

MDPI AG
Algorithms Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
algorithms@mdpi.com
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Algorithms
Back to Top