A Decision-Making Framework for Control Strategies
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Authors
Chung, Timothy H.
Burdick, Joel W.
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2007
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Abstract
This paper presents the search problem formulated as a decision problem, where the searcher decides whether the target is present in the search region, and if so, where it is located. Such decision-based search tasks are relevant to many research areas, including mobile robot missions, visual search and attention, and event detection in sensor networks. The effect of control strategies in search problems on decisionmaking quantities, namely time-to-decision, is investigated in this work. We present a Bayesian framework in which the objective is to improve the decision, rather than the sensing, using different control policies. Furthermore, derivations of closed-form expressions governing the evolution of the belief function are also presented. As this framework enables the study and comparison of the role of control for decision-making applications, the derived theoretical results provide greater insight into the sequential processing of decisions. Numerical studies are presented to verify and demonstrate these results.
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Conference Paper
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In Probabilistic Search. In Proc. of IEEE Intl. Conference on Robotics and Automation, 2007.
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Citation
Timothy H. Chung and Joel W. Burdick. A Decision-Making Framework for Control Strategies in Probabilistic Search. In Proc. of IEEE Intl. Conference on Robotics and Automation, 2007.
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.