A Satisficing, Negotiated, and Learning Coalition Formation Architecture

  • Leen-Kiat Soh
  • Costas Tsatsoulis
  • Huseyin Sevay
Part of the Multiagent Systems, Artificial Societies, and Simulated Organizations book series (MASA, volume 9)


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.


Multiagent System Task Allocation Coalition Formation Negotiation Strategy Coalition Member 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Leen-Kiat Soh
    • 1
  • Costas Tsatsoulis
    • 2
  • Huseyin Sevay
    • 2
  1. 1.Computer Science and Engineering DepartmentUniversity of NebraskaLincolnUSA
  2. 2.Information and Telecommunication Technology Center (ITTC), Department of Electrical Engineering and Computer ScienceThe University of KansasLawrenceUSA

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