摘要翻译：
我们考虑以下顺序决策问题。给定一组效用未知的项目，我们需要从效用尽可能高的项目中选择一个(`选择问题')。在已知的成本下，允许在选择之前测量项目值（可能有噪声）。目标是优化测量和选择的整体顺序决策过程。信息值(VOI)是一个众所周知的测量选择方案，但问题的复杂性通常导致使用短视的VOI估计。在选择问题中，短视的VOI往往严重低估了信息的价值，导致低质量的感知方案。我们将严格的短视假设放宽为半短视方案，提供了一系列可以提高感知计划性能的方法。特别地，我们提出了有效的“Blinkered”VOI计算方法，并研究了特殊情况下的理论界。在正态分布项值的选择问题中，对“闪烁”VOI的经验评估表明，is的表现远优于纯近视VOI。
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英文标题：
《Semi-Myopic Sensing Plans for Value Optimization》
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作者：
David Tolpin, Solomon Eyal Shimony
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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 the following sequential decision problem. Given a set of items of unknown utility, we need to select one of as high a utility as possible (``the selection problem''). Measurements (possibly noisy) of item values prior to selection are allowed, at a known cost. The goal is to optimize the overall sequential decision process of measurements and selection.   Value of information (VOI) is a well-known scheme for selecting measurements, but the intractability of the problem typically leads to using myopic VOI estimates. In the selection problem, myopic VOI frequently badly underestimates the value of information, leading to inferior sensing plans. We relax the strict myopic assumption into a scheme we term semi-myopic, providing a spectrum of methods that can improve the performance of sensing plans. In particular, we propose the efficiently computable method of ``blinkered'' VOI, and examine theoretical bounds for special cases. Empirical evaluation of ``blinkered'' VOI in the selection problem with normally distributed item values shows that is performs much better than pure myopic VOI.
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PDF链接：
https://arxiv.org/pdf/0906.3149