[计算机科学] 泛人工智能期望效用的收敛性 [推广有奖]

摘要翻译：
我们考虑一个agent和一个环境之间的一系列重复交互。环境的不确定性是由一个假设空间上的概率分布所捕获的，这个假设空间包括所有可计算的函数。给定一个效用函数，我们可以评估任何与环境交互的计算策略的期望效用。在做了一些合理的假设（也许还有一个不太合理的假设）之后，我们证明了如果效用函数是无界的，那么任何策略的期望效用都是未定义的。
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英文标题：
《Convergence of Expected Utility for Universal AI》
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作者：
Peter de Blanc
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  We consider a sequence of repeated interactions between an agent and an environment. Uncertainty about the environment is captured by a probability distribution over a space of hypotheses, which includes all computable functions. Given a utility function, we can evaluate the expected utility of any computational policy for interaction with the environment. After making some plausible assumptions (and maybe one not-so-plausible assumption), we show that if the utility function is unbounded, then the expected utility of any policy is undefined.
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PDF链接：
https://arxiv.org/pdf/0907.5598