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action agent AI/i AIXI model algorithmic information theory approximable arbitrary argument assume assumption asymptotically axioms Bayes mixtures binary chain rule Chapter chronological semimeasure coding conditional probabilities constant convergence countable cycle decision theory defined definition depends deterministic discount enumerable semimeasures environment ergodic MDPs error bounds exists expected reward feedback finitely computable formal hence horizon i-expected implies induction inequality infinite input intelligence interpreted Kolmogorov complexity Lemma length Levin search loss bounds loss functions machine learning maximize measure minimax minimization number of errors Occam's razor optimal policy output yk Pareto optimality performance player prediction scheme predictor prior probability probabilistic probability distribution probability theory problem classes proof prove recursive reinforcement learning restricted reward sum Section self-optimizing policies sense sequence prediction sequential decision theory Solomonoff solve string supervised learning Theorem true universal prior universal Turing machine weights xi:oo zero
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JÜRGEN SCHMIDHUBER - 2006 - Connection Science
Marcus Hutter, Jan Poland - 2005 - Arxiv preprint cs.AI/0504078
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Shane Legg, Marcus Hutter - 2006 - Arxiv preprint cs.AI/0605024
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