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1
Feature reinforcement learning using looping suffix trees
Daswani, Mayank; Sunehag, Peter; Hutter, Marcus. - : Journal of Machine Learning Research, 2015
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2
Consistency of Feature Markov Processes
Sunehag, Peter; Hutter, Marcus. - : Springer Verlag, 2015
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3
A Monte-Carlo AIXI Approximation
In: Journal of Artificial Intelligence Research (2015)
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4
A Monte-Carlo AIXI Approximation
In: Journal of Artificial Intelligence Research (2015)
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5
Reinforcement learning via AIXI Approximation
Veness, Joel; Ng, Kee Siong; Hutter, Marcus. - : AAAI Press, 2015
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6
Feature Reinforcement Leaning using Looping Suffix Trees
In: JMLR: Workshop and Conference Proceedings (2015)
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7
Reinforcement learning via AIXI Approximation
Veness, Joel; Ng, Kee Siong; Hutter, Marcus. - : AAAI Press, 2015
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8
Feature Reinforcement Leaning using Looping Suffix Trees
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9
Consistency of Feature Markov Processes
Sunehag, Peter; Hutter, Marcus. - : Springer Verlag, 2015
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10
Feature reinforcement learning using looping suffix trees
Daswani, Mayank; Sunehag, Peter; Hutter, Marcus. - : Journal of Machine Learning Research, 2015
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11
Tests of machine intelligence
Legg, Shane; Hutter, Marcus. - : Springer Verlag, 2015
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12
Can intelligence explode?
In: Journal of Consciousness Studies (2015)
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13
Universal intelligence: A definition of machine intelligence
In: Minds and Machines: journal for artificial intelligence, philosophy and cognitive sciences (2015)
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14
Can intelligence explode?
In: Journal of Consciousness Studies (2015)
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15
Universal intelligence: a definition of machine intelligence
In: Minds and Machines (2015)
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16
Universal intelligence: a definition of machine intelligence
In: Minds and Machines (2015)
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17
Open problems in universal induction & intelligence
In: Algorithms (2015)
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 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.
Keyword: artificial intelligence; information theory; Kolmogorov complexity; rational agents; reinforcement learning; sequential decision theory; universal Solomonoff induction
URL: http://hdl.handle.net/1885/14922
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18
Open Problems in Universal Induction & Intelligence
In: Algorithms (2015)
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19
Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009
Goertzel, Ben; Hitzler, Pascal; Hutter, Marcus. - : Atlantis Press, 2015
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20
Universal intelligence: A definition of machine intelligence
In: Minds and Machines: journal for artificial intelligence, philosophy and cognitive sciences (2015)
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