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Towards a Universal Theory of Artificial Intelligence based on Algorithmic Probability and Sequential Decision Theory (2000)  (Make Corrections)  (3 citations)
Marcus Hutter
Lecture Notes in Computer Science



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Abstract: Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental probability distribution is known. (Update)

Context of citations to this paper:   More

...been introduced and discussed in March 2000 in [Hut00] in a 62 page long report. More succinct descriptions have been published in [Hut01d, Hut01e]. The AI# model has been argued to formally solve a number of problem classes, including sequence prediction, strategic games,...

...an environment can also use predictions of the future to compute action sequences that maximize expected future reward. Hutter s AIXI model [10] does exactly this, by combining Solomonoff s M based universal prediction scheme with an expectimax computation. It can be shown...

Cited by:   More
Optimality of Universal Bayesian Sequence Prediction for General.. - Hutter (2003)   (Correct)
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A Gentle Introduction to the Universal Algorithmic Agent AIXI - Hutter (2003)   (Correct)

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BibTeX entry:   (Update)

M. Hutter. Towards a universal theory of artificial intelligence based on algorithmic probability and sequential decisions. Proceedings of the 12 Eurpean Conference on Machine Learning (ECML-2001), pages 226--238, 2001. http://citeseer.ist.psu.edu/hutter00towards.html   More

@article{ hutter01towards,
    author = "Marcus Hutter",
    title = "Towards a Universal Theory of Artificial Intelligence Based on Algorithmic Probability and Sequential Decisions",
    journal = "Lecture Notes in Computer Science",
    volume = "2167",
    pages = "226--??",
    year = "2001",
    url = "citeseer.ist.psu.edu/hutter00towards.html" }
Citations (may not include all citations):
660   An introduction to Kolmogorov complexity and its application.. - Li, Vit'anyi - 1997
614   Reinforcement learning: An introduction - Sutton, Barto - 1998
417   Stochastic Complexity in Statistical Inquiry (context) - Rissanen - 1989
413   NeuroDynamic Programming (context) - Bertsekas, Tsitsiklis - 1996
408   Princeton University Press (context) - Bellman - 1957
278   Dynamic Programming and Optimal Control (context) - Bertsekas - 1995
159   Three approaches to the quantitative definition of informati.. (context) - Kolmogorov - 1965
110   A theory of program size formally identical to information t.. - Chaitin - 1975
81   Universal prediction of individual sequences (context) - Feder, Merhav et al. - 1992
64   Universal sequential search problems (context) - Levin - 1973
44   Problems of Information Transmission (context) - Levin, information et al. - 1974
43   Randomness conservation inequalities: Information and indepe.. (context) - Levin - 1984
42   Learning simple concepts under simple distributions - Li, Vit'anyi - 1991
40   Inductive reasoning and Kolmogorov complexity (context) - Li, Vit'anyi - 1992
36   Discovering neural nets with low Kolmogorov complexity and h.. - Schmidhuber - 1997
33   Shifting inductive bias with success-story algorithm - Schmidhuber, Zhao et al. - 1997
31   Complexity-based induction systems: comparisons and converge.. (context) - Solomonoff - 1978
30   Artificial Intelligence (context) - Russell, Norvig - 1995
8   A search for the missing science of consciousness (context) - Penrose, the - 1994
6   Algorithmic information and evolution (context) - Chaitin - 1991
5   Optimality of universal prediction for general loss and alph.. (context) - Hutter - 2000
4   A theory of universal artificial intelligence based on algor.. - Hutter - 2000
3   formerly Soviet Mathematics-- Doklady (context) - G'acs, symmetry et al. - 1974
2   A formal theory of inductive inference: Part 1 and (context) - Solomonoff - 1964
2   Reinforcement learning: a survey (context) - Moore, Kaelbling et al. - 1996
2   New error bounds for Solomonoff prediction - Hutter - 1999

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