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2001-
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SNF
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Unification of Inductive Inference and sequential decision theory
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The goal is to develop a universal theory of sequential decision making akin to Solomonoff's celebrated universal theory of induction. Solomonoff derived an optimal way of predicting future data, given previous observations, provided the data is sampled from a recursively computable probability distribution. The goal is to extend this approach to derive an optimal rational reinforcement learning agent embedded in an unknown environment sampled from a computable distribution. One remarkable and surprising by-product of this research is the asymptotically fastest and shortest algorithm for solving ANY well-defined problem.
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Marcus Hutter
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Coordinator
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Juergen Schmidhuber
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