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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)
Abstract: A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines. ; SNF grant 200020-107616.
Keyword: AIXI; Complexity theory; Intelligence; Intelligence tests; Measures Definitions; Theoretical foundations; Turing test
URL: http://hdl.handle.net/1885/14995
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17
Open problems in universal induction & intelligence
In: Algorithms (2015)
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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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