CAREER: Light-weight Near-optimal Stochastic Control Policies for Information Acquisition and Exploitation

职业：用于信息获取和利用的轻量级近最优随机控制策略

• 批准号: 0745761
• 负责人: Kameshwar Munagala
• 金额: $ 40万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0745761&HistoricalAwards=false
• 关键词: CAREER; Light; weight; Near; optimal

Dr. Munagala will develop theoretical models for the problems ofuncertainty and imprecise information in computing and communicationsystems such as wireless sensor networks and grid computers, anddesign algorithms and policies to ameliorate their impact on systemperformance, thereby leading to more effective larger-scaledeployments of such systems.He will also develop algorithms for computing dynamic informationacquisition strategies in uncertain environments, that jointlyoptimize the cost of acquiring more information, together with thegain from exploiting the information to enhance systemperformance. This class of problems falls within the larger area ofstochastic control theory.It is well known that computing and storing the optimal controlpolicies (or strategies) needs exponential time and space. However, inthe scenarios that Dr. Munagala will investigate, the system cantolerate certain suboptimality in the quality of planning decisions,but the real requirement is that the policies be extremely lightweightto compute and execute. This motivates the formulation and design ofcomputationally efficient approximately optimal policies for an entiregamut of fundamental control theoretic problems.The PI will draw upon his expertise in approximation and onlinealgorithms to develop new insights to address important theoreticalquestions in stochastic control theory, with emphasis on informationacquisition and exploitation problems. In contrast with techniquesdeveloped in the AI and operations research communities, the solutionsdeveloped will have provable performance guarantees, in addition tobeing efficient to implement. The novel algorithmic principlesemerging from this project will lay the foundations for developing afresh theory of approximate stochastic control.

Munagala博士将为计算和通信系统（如无线传感器网络和网格计算机）中的不确定性和不精确信息问题开发理论模型，并设计算法和策略来改善它们对系统性能的影响，从而导致此类系统更有效的大规模部署。他还将开发在不确定环境中计算动态信息获取策略的算法，这两个因素共同优化了获取更多信息的成本，以及利用信息提高系统性能的收益。这类问题福尔斯属于随机控制理论的一个较大的范畴，众所周知，计算和存储最优控制策略（或策略）需要指数级的时间和空间。然而，在Munagala博士将研究的场景中，系统可以容忍规划决策质量的某些次优，但真实的要求是策略的计算和执行非常轻便。这促使制定和设计的计算效率近似最优的政策，为整个regamut的基本控制理论问题。PI将利用他的专业知识，在近似和在线算法开发新的见解，以解决重要的理论问题，在随机控制理论，重点是信息采集和开发问题。与人工智能和运筹学领域开发的技术相比，开发的解决方案除了能够有效实施外，还将具有可证明的性能保证。该项目提出的新的算法原理将为发展新的近似随机控制理论奠定基础。