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Dynamic Decision Making under Uncertainty and Partial Information

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Georgia Institute of Technology Atlanta United States

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This project is concerned with the study of basic questions aimed at meeting challenges in information superiority, logistics, and planning for the Air Force of the future. For successful military operations, the future requirements of the Air Force will include information fusion at a much larger scale and much more agile, responsive, and integrated systems. Such problems and systems are exceedingly complex however, a central part of them is decision making, which often takes place sequentially in time, subject to uncertainty in the future and limited partial information at hand. In order to address these problems, we investigated efficient computational methodologies for dynamic decision making under uncertainty and partial information. In the course of this research, we developed and studied efficient simulation-based methodologies for dynamic decision making under uncertainty and partial information ii studied the application of these decision making models and methodologies to practical problems, such as those arising in planning, logistics, and risk management. The proposed research resulted in i new mathematical tools and theories for dynamic decision making and optimal stopping ii useful application of these models and new methodologies in a wide range of problems.

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Technical Report,02 Jan 2014,31 Jan 2017




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Approved For Public Release;

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