摘要翻译：
计算信息价值(VOI)是不确定性条件下决策的各个方面的关键任务，如搜索的元推理；在选择措施时，在选择行动方针之前；在管理勘探与开发的权衡中。由于这样的应用程序通常在一次运行期间需要大量的VOI计算，所以有效地计算VOI是至关重要的。我们研究了VOI的任意时间估计问题，因为通常只需得到VOI的粗略估计就足够了，从而节省了大量的计算资源。作为一个案例研究，我们考察了测量选择问题中的VOI估计。对该方案在该领域的经验评估表明，该方案确实可以显著减少计算资源，并在总体决策问题中以较小的代价实现预期回报。
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英文标题：
《Rational Value of Information Estimation for Measurement Selection》
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作者：
David Tolpin and Solomon Eyal Shimony
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最新提交年份：
2010
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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：
  Computing value of information (VOI) is a crucial task in various aspects of decision-making under uncertainty, such as in meta-reasoning for search; in selecting measurements to make, prior to choosing a course of action; and in managing the exploration vs. exploitation tradeoff. Since such applications typically require numerous VOI computations during a single run, it is essential that VOI be computed efficiently. We examine the issue of anytime estimation of VOI, as frequently it suffices to get a crude estimate of the VOI, thus saving considerable computational resources. As a case study, we examine VOI estimation in the measurement selection problem. Empirical evaluation of the proposed scheme in this domain shows that computational resources can indeed be significantly reduced, at little cost in expected rewards achieved in the overall decision problem.
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PDF链接：
https://arxiv.org/pdf/1003.5305