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Game Theoretic Optimal Sensor Deployment

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Distributed Optimization-Based Control of Multi-Agent Networks in Complex Environments

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSCONTROL))

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Abstract

In noncooperative games, players are self-interested and aim to maximize their own utilities. The selfishness makes the decision-making of players naturally distributed. This attractive feature is evident in a large number of game theoretic learning algorithms, e.g., better and best reply dynamics. As a result, game theoretic learning provides a powerful arsenal for the synthesis of efficient distributed algorithms to multi-agent networks. This chapter will discuss one of its applications to sensor deployment.

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Notes

  1. 1.

    See [4] for a definition of proximity graph.

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Authors and Affiliations

  1. Department of Electrical Engineering, Pennsylvania State University, University Park, PA, USA

    Minghui Zhu

  2. Department of Mechanical and Aerospace Engineering, University of California, San Diego, La Jolla, CA, USA

    Sonia Martínez

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  1. Minghui Zhu
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  2. Sonia Martínez
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Correspondence to Minghui Zhu .

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Zhu, M., Martínez, S. (2015). Game Theoretic Optimal Sensor Deployment. In: Distributed Optimization-Based Control of Multi-Agent Networks in Complex Environments. SpringerBriefs in Electrical and Computer Engineering(). Springer, Cham. https://doi.org/10.1007/978-3-319-19072-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-19072-3_3

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