Sufficient statistics for team decision problems
- Decentralized control problems involve multiple controllers, each having access to different measurements but working together to optimize a common objective. Despite being extremely difficult to solve in general, a common thread behind the more tractable cases is the identification of sufficient statistics for each controller, i.e. reductions of the measurements for each controller that do not sacrifice optimal performance. These sufficient statistics serve to greatly reduce the controller search space, thus making the problem easier to solve. In this dissertation, we develop for the first time a general theory of sufficient statistics for team decision problems, a fundamental type of decentralized control problem. We give rigorous definitions for team decisions and team sufficient statistics, and show how team decisions based only on these sufficient statistics do not affect optimal performance. In a similar spirit to the Kalman filter, we also show how to gracefully update the team sufficient statistics as the state evolves and additional measurements are collected. Finally, we show how to compute team sufficient statistics for partially nested problems, a large class of team decision problems that tend to have easier solutions. These team sufficient statistics have intuitive and compelling interpretations. We also show general conditions when these team sufficient statistics can be updated without the state growing in size. To illustrate the results, we give examples for finite-state systems and systems whose variables are jointly Gaussian.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Stanford University, Department of Electrical Engineering.
|Van Roy, Benjamin
|Van Roy, Benjamin
|Statement of responsibility
|Submitted to the Department of Electrical Engineering.
|Thesis (Ph.D.)--Stanford University, 2013.
- © 2013 by Jeffrey Noel Wu
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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