CIF: Small: Controlling Uncertainty: On the Sequential Refinement of Belief

CIF：小：控制不确定性：关于信念的顺序完善

• 批准号: 1018722
• 负责人: Tara Javidi
• 金额: $ 38.06万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018722&HistoricalAwards=false
• 关键词: CIF; Small; Controlling; Uncertainty; Sequential

Controlling Uncertainty: On the Sequential Refinement of BeliefThis research focuses on a variety of information acquisition and sensing applications in which adecision maker, by carefully controlling a sequence of actions with uncertain outcomes,dynamically refines his belief about a phenomenon of interest in a speedy, accurate, and efficientmanner. The model includes a class of applications in communications, design of experiments,cognitive science, and sensor management. In particular, the following three problems aretackled.? Active Sequential Hypothesis Testing: There are a set of hypotheses, one of which is true. Adecision maker is asked to identify the correct hypothesis by sequentially employing either oneof available sensing actions. Actions costs differently and produce statistically distinctobservations. Given a penalty for error in declaration, the work investigates the optimalselection of sensing actions.? Feedback Schemes for Joint Source-Channel Coding with Bandwidth Expansion: A message isto be conveyed to a receiver over a noisy memoryless channel with feedback. The expecteddistortion between the message and the receiver?s construction is sought to be minimized overthe choice of causal encoding functions as well as the decoding function.? Joint Source-Channel Coding over a Multiple Access Channel with Feedback: Multipletransmitters convey messages to a common receiver over a noisy memoryless multiple accesschannel with perfect output feedback.These problems boil down to the sequential control of a dynamical system whose system is theconditional distribution of the unknown (true hypothesis, message, etc) and whose dynamics isdictated by the Bayes? rule. In particular, the optimal choice of actions, i.e. refinement in theconditional distribution and reduction of uncertainty, is investigated.

控制不确定性：关于信念的顺序精化本研究专注于各种信息获取和感知应用，在这些应用中，决策者通过仔细控制一系列具有不确定结果的动作，以快速、准确和高效的方式动态地精炼他对感兴趣的现象的信念。该模型包括一类在通信、实验设计、认知科学和传感器管理方面的应用。特别是，解决了以下三个问题。主动序贯假设检验：有一组假设，其中一个是真的。决策制定者被要求通过顺序地使用其中一个可用的感知动作来识别正确的假设。行动的成本不同，产生的统计结果也不同。给出了对声明错误的惩罚，该工作调查了传感动作的最佳选择。带带宽扩展的联合信源-信道编码的反馈方案：在有反馈的噪声无记忆信道上将消息传送到接收器。在对因果编码函数和解码函数的选择上，寻求最小化消息和接收者之间的期望失真？？S结构。带反馈的多址信道上的联合信源-信道编码：多路发射机通过具有完美输出反馈的噪声无记忆多址信道将消息传送到公共接收机。这些问题归结为对一个动态系统的顺序控制，该系统的系统是未知(真实假设、消息等)的条件分布，其动态由贝叶斯决定。规则。特别地，研究了行动的最优选择，即条件分布的精化和不确定性的减少。