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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)
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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
Abstract: Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence.
URL: http://hdl.handle.net/1885/14963
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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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