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arXiv:1105.5721cs.LG

A Philosophical Treatise of Universal Induction

Samuel Rathmanner, Marcus Hutter

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Abstract

Understanding inductive reasoning is a problem that has engaged mankind for thousands of years. This problem is relevant to a wide range of fields and is integral to the philosophy of science. It has been tackled by many great minds ranging from philosophers to scientists to mathematicians, and more recently computer scientists. In this article we argue the case for Solomonoff Induction, a formal inductive framework which combines algorithmic information theory with the Bayesian framework. Although it achieves excellent theoretical results and is based on solid philosophical foundations, the requisite technical knowledge necessary for understanding this framework has caused it to remain largely unknown and unappreciated in the wider scientific community. The main contribution of this article is to convey Solomonoff induction and its related concepts in a generally accessible form with the aim of bridging this current technical gap. In the process we examine the major historical contributions that have led to the formulation of Solomonoff Induction as well as criticisms of Solomonoff and induction in general. In particular we examine how Solomonoff induction addresses many issues that have plagued other inductive systems, such as the black ravens paradox and the confirmation problem, and compare this approach with other recent approaches.

arXiv comments
72 pages, 2 figures, 1 table, LaTeX
Journal reference
Entropy, 13:6 (2011) pages 1076-1136
DOI
doi:10.3390/e13061076
Subjects
cs.LGMachine Learning cs.ITInformation Theory

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    • Solomonoff induction seems to be an useful innate bias for the current approaches at general intelligence. It's basically a formalization of Occam's razor that states that instead of being completely random, the surrounding environment is mostly lawful and simple, and therefore…

      rsaarelmView on HNParent context

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