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

基本信息

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

项目摘要

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：关于信仰的顺序完善，这项研究的重点是各种信息获取和感应应用，在这些信息中，通过仔细控制具有不确定结果的一系列动作，Adecision Maker动态地完善了他对快速，准确，准确和高效的人的兴趣现象的信念。该模型包括一类用于通信，实验设计，认知科学和传感器管理的应用程序。特别是以下三个问题。主动顺序假设检验：有一组假设，其中之一是正确的。要求Adecision Maker通过顺序采用任何一种可用的传感动作来识别正确的假设。动作的成本不同，并产生统计上不同的行动。鉴于声明错误的惩罚，该工作调查了传感动作的最佳选择。具有带宽扩展的联合源通道编码的反馈方案：通过带有反馈的嘈杂的无内存通道，将传达给接收器的消息。在选择因果编码函数以及解码功能的选择中，寻求消息和接收器的构造之间的预期限制。通过反馈的多个访问通道上的联合源渠道编码：多通信器通过具有完美输出反馈的嘈杂的无内存多访问通道传达通用接收器的消息。这些问题归结为对动态系统的顺序控制，其系统是未知的系统分布的动态系统（真实的假设，消息，等）和Bayes by bayes bayes bayes bayes bayes bayes bayes？规则。特别是，研究了最佳的作用选择，即研究条件分布和降低不确定性。