Special Issue ""Algorithmic Complexity in Physics & Embedded Artificial Intelligences"—In Memoriam Ray Solomonoff (1926-2009)"
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (31 August 2010)
Prof. Dr. Juergen Schmidhuber
IDSIA, Galleria 2, 6928 Manno-Lugano, Switzerland
Phone: +41 58 666666 2
Interests: artificial intelligence; machine learning; neural networks; Kolmogorow-complexity; robotics
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
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed Open Access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 300 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.
- algorithmic complexity
- algorithmic information theory
- Kolmogorov complexity
- descriptive complexity
- Kolmogorov-Chaitin complexity
- stochastic complexity
- algorithmic entropy
- program-size complexity
- Chaitin entropy
- Chaitin complexity
Algorithms 2009, 2(3), 879-906; doi:10.3390/a2030879
Received: 8 April 2009; in revised form: 15 June 2009 / Accepted: 16 June 2009 / Published: 2 July 2009| Download PDF Full-text (255 KB)
Obituary: 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| Download PDF Full-text (84 KB)
Algorithms 2010, 3(3), 260-264; doi:10.3390/a3030260
Received: 12 March 2010 / Accepted: 14 March 2010 / Published: 20 July 2010| Download PDF Full-text (68 KB)
Algorithms 2010, 3(4), 329-350; doi:10.3390/a3040329
Received: 30 August 2010 / Accepted: 22 September 2010 / Published: 29 September 2010| Download PDF Full-text (303 KB)
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: MML, Occam's razor, explanation, prediction, entropy, time's arrow, intelligent design and miracles
Author: David Dowe; email@example.com
Abstract: We introduce Minimum Message Length (MML) (Wallace & Boulton, 1968) as a statistically invariant and statistically consistent Bayesian interpretation of algorithmic information theory and Occam's razor. Despite problems of other approaches with retaining statistical consistency under model misspecification (Grunwald & Langford, 2007), no known evidence exists yet of a problem for MML here. We explore the frequently misunderstood distinction between inference (or induction, or explanation) and prediction. We then focus on Wallace's idea (2005) that, given CPT-invariance in Physics, it appears that - contrary to wide-held beliefs - entropy is not time's arrow. We then address why it is that people wish to predict the future but (only) infer (or explain) the past. We draw attention to MML's ability to predict better than rival methods while using simpler models, mentioning some areas of application in physical models. We offer some scepticism about quantum computing, and we use MML to address evidence of "intelligent design'' (Dowe et al., 2009) and miracles (Dowe, 2008a).
Last update: 18 February 2011