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A framework for coordination and learning among teams of agents

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Agents and Multi-Agent Systems Formalisms, Methodologies, and Applications (DAI 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1441))

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Abstract

We present a framework for team coordination under incomplete information based on the theory of incomplete information games. When the true distribution of the uncertainty involved is not known in advance, we consider a repeated interaction scenario and show that the agents can learn to estimate this distribution and share their estimations with one another. Over time, as the set of agents' estimations become more accurate, the utility they can achieve approaches the optimal utility when the true distribution is known, while the communication requirement for exchanging the estimations among the agents can be kept to a minimal level.

Supported by the Australian Government's Overseas Postgraduate Research Scholarship (OPRS) and Curtin University Postgraduate Scholarship (CUPS).

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Author information

Authors and Affiliations

  1. Department of Computer Science, Curtin University of Technology, 6001, Perth, WA, Australia

    Hung H. Bui, Svetha Venkatesh & Dorota Kieronska

Authors
  1. Hung H. Bui
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  2. Svetha Venkatesh
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  3. Dorota Kieronska
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Editor information

Wayne Wobcke Maurice Pagnucco Chengqi Zhang

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© 1998 Springer-Verlag Berlin Heidelberg

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Bui, H.H., Venkatesh, S., Kieronska, D. (1998). A framework for coordination and learning among teams of agents. In: Wobcke, W., Pagnucco, M., Zhang, C. (eds) Agents and Multi-Agent Systems Formalisms, Methodologies, and Applications. DAI 1997. Lecture Notes in Computer Science, vol 1441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0055027

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  • DOI: https://doi.org/10.1007/BFb0055027

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64769-0

  • Online ISBN: 978-3-540-68722-1

  • eBook Packages: Springer Book Archive

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