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A Satisficing, Negotiated, and Learning Coalition Formation Architecture

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Distributed Sensor Networks

Abstract

In this chapter, we present a multiagent system architecture for dynamic coalition formation and coalition strategy learning in a realtime multisensor target tracking environment. Agents operate autonomously, and they have incomplete information about their potential collaborators. In addition, accurate target tracking requires that multiple agents recognize and synchronize their actions-collecting measurements on the same target within the same time frame. Therefore some form of cooperation is necessary. In our system, agents form coalitions via multiple 1-to-l negotiations. However, due to the noisy and uncertain properties of the environment, coalitions formed can be only suboptimal and satisficing. To better adapt to changing requirements and environment dynamics, each agent is capable of multiple levels of learning. Each learns about how to negotiate better (case-based learning) and how to form a coalition better (reinforcement learning). To increase the chance of reaching a high-quality negotiated deal, our work also addresses issues in task allocation and dynamic utility-based profiling.

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Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, University of Nebraska, 115 Ferguson Hall, Lincoln, NE, 68588, USA

    Leen-Kiat Soh

  2. Information and Telecommunication Technology Center (ITTC), Department of Electrical Engineering and Computer Science, The University of Kansas, 2335 Irving Hill Road, Lawrence, KS, 66045, USA

    Costas Tsatsoulis & Huseyin Sevay

Authors
  1. Leen-Kiat Soh
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  2. Costas Tsatsoulis
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  3. Huseyin Sevay
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Editor information

Editors and Affiliations

  1. University of Massachusetts, USA

    Victor Lesser

  2. SRI International, USA

    Charles L. Ortiz Jr.

  3. University of Southern California, USA

    Milind Tambe

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© 2003 Springer Science+Business Media New York

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Soh, LK., Tsatsoulis, C., Sevay, H. (2003). A Satisficing, Negotiated, and Learning Coalition Formation Architecture. In: Lesser, V., Ortiz, C.L., Tambe, M. (eds) Distributed Sensor Networks. Multiagent Systems, Artificial Societies, and Simulated Organizations, vol 9. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0363-7_7

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  • DOI: https://doi.org/10.1007/978-1-4615-0363-7_7

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-5039-2

  • Online ISBN: 978-1-4615-0363-7

  • eBook Packages: Springer Book Archive

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