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On the computability of Solomonoff induction and AIXI

机译:关于所罗门组织诱导和赤霞的计算性

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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. We quantify this using the arithmetical hierarchy, and prove upper and in most cases corresponding lower bounds for incomputability. Moreover, we show that AIXI is not limit computable, thus it cannot be approximated using finite computation. However there are limit computable epsilon-optimal approximations to AIXI. We also derive computability bounds for knowledge seeking agents, and give a limit computable weakly asymptotically optimal reinforcement learning agent. Crown Copyright (C) 2017 Published by Elsevier B.V. All rights reserved.
机译:如果我们有无限计算,我们如何解决机器学习和人工智能问题? Solomonoff Incuction和钢筋学习代理AIXI是这个问题的答案。 众所周知,两者都是可计算的。 我们使用算术层次结构量化这一点,并证明了上部,大多数情况下对应的计数性的相应下限。 此外,我们表明AIXI不是限制可计算,因此不能使用有限计算来近似。 然而,对AIXI有极限的可计算epsilon - 最佳近似。 我们还导出知识寻求代理的可计算性范围,并提供极限可计算的弱点渐近最佳的加强学习代理。 皇冠版权(c)2017由elsevier b.v出版。保留所有权利。

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