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

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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.