On the Computability of AIXI

October 19, 2015 Β· Declared Dead Β· πŸ› Conference on Uncertainty in Artificial Intelligence

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Authors Jan Leike, Marcus Hutter arXiv ID 1510.05572 Category cs.AI: Artificial Intelligence Citations 9 Venue Conference on Uncertainty in Artificial Intelligence Last Checked 3 months ago
Abstract
How could we solve the machine learning and the artificial intelligence problem if we had infinite computation? Solomonoff induction and the reinforcement learning agent AIXI are proposed answers to this question. Both are known to be incomputable. In this paper, we quantify this using the arithmetical hierarchy, and prove upper and corresponding lower bounds for incomputability. We show that AIXI is not limit computable, thus it cannot be approximated using finite computation. Our main result is a limit-computable Ξ΅-optimal version of AIXI with infinite horizon that maximizes expected rewards.
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