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Abstract: Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental prior probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown prior distribution. We combine both ideas and get a parameterless theory of universal Artificial Intelligence. We give strong arguments that the resulting AIXI model is the most intelligent unbiased agent possible. We outline for a number of... (Update)
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.... results for universal learning machines, essentially ignoring issues of practical feasibility compare my postdoc Hutter s contribution [25] in this volume. Section 5, however, will focus on our recent non traditional simplicity measure which is not based on the shortest...
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BibTeX entry: (Update)
M. Hutter. A gentle introduction to the universal algorithmic agent AIXI. In B. Goertzel and C. Pennachin, editors, Real AI: New Approaches to Arti cial General Intelligence. Plenum Press, New York, 2003. To appear. http://citeseer.ist.psu.edu/hutter03gentle.html More
@incollection{ hutter:03aixigentle,
author = "Marcus Hutter",
title = "A Gentle Introduction to The Universal Algorithmic Agent {AIXI}",
booktitle = "Artificial General Intelligence",
editor = "B. Goertzel and C. Pennachin",
number = "IDSIA-01-03",
institution = "IDSIA",
year = "2003",
pages = "70",
url = "citeseer.ist.psu.edu/hutter03gentle.html",
url = "http://www.hutter1.de/ai/aixigentle.htm",
http = "http://arxiv.org/abs/cs.AI/03?????",
ftp = "ftp://ftp.idsia.ch/pub/techrep/IDSIA-01-03.ps.gz",
keywords = "Artificial intelligence; algorithmic probability;
sequential decision theory; rational agents;
value function; Solomonoff induction;
Kolmogorov complexity; reinforcement learning;
universal sequence prediction; strategic games;
function minimization; supervised learning." }
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