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dc.contributor.authorGreigarn, Tipakorn
dc.contributor.authorBranicky, Michael S.
dc.contributor.authorÇavuşoğlu, M. Cenk
dc.identifier.citationT. Greigarn, M. S. Branicky and M. C. Çavuşoğlu, "Task-Oriented Active Sensing via Action Entropy Minimization," in IEEE Access, vol. 7, pp. 135413-135426, 2019. doi: 10.1109/ACCESS.2019.2941706en_US
dc.descriptionThis work is licensed under a Creative Commons Attribution 4.0 International License.en_US
dc.description.abstractIn active sensing, sensing actions are typically chosen to minimize the uncertainty of the state according to some information-theoretic measure such as entropy, conditional entropy, mutual information, etc. This is reasonable for applications where the goal is to obtain information. However, when the information about the state is used to perform a task, minimizing state uncertainty may not lead to sensing actions that provide the information that is most useful to the task. This is because the uncertainty in some subspace of the state space could have more impact on the performance of the task than others, and this dependence can vary at different stages of the task. One way to combine task, uncertainty, and sensing, is to model the problem as a sequential decision making problem under uncertainty. Unfortunately, the solutions to these problems are computationally expensive. This paper presents a new task-oriented active sensing scheme, where the task is taken into account in sensing action selection by choosing sensing actions that minimize the uncertainty in future task-related actions instead of state uncertainty. The proposed method is validated via simulations.en_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsCopyright 2019 The Authors.en_US
dc.subjectActive sensingen_US
dc.subjectDecision makingen_US
dc.titleTask-Oriented Active Sensing via Action Entropy Minimizationen_US
kusw.kuauthorBranicky, Michael S.
kusw.kudepartmentEngineering Administrationen_US
kusw.kudepartmentElectrical Engineering & Computer Scienceen_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US

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Copyright 2019 The Authors.
Except where otherwise noted, this item's license is described as: Copyright 2019 The Authors.