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

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，共同优化获取更多信息的成本，以及从利用信息中获得的收益，以提高系统性能。这类问题属于随机控制理论的更大范围。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博士将要研究的场景中，系统可以容忍规划决策质量的某些次优性，但真正的要求是策略的计算和执行非常轻量级。这激发了对大量基本控制理论问题的计算效率近似最优策略的制定和设计。PI将利用他在近似和在线算法方面的专业知识来开发新的见解，以解决随机控制理论中的重要理论问题，重点是信息获取和利用问题。与人工智能和运筹学社区开发的技术相比，开发的解决方案除了有效实施外，还将具有可验证的性能保证。从这个项目中产生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。

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.