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Compact approximations of mixture distributions for state estimation in multiagent settings

Published: 10 May 2009 Publication History

Abstract

In order to act rationally, an agent must track the state of the environment over time. In the presence of other agents who themselves act, observe, and update their beliefs the agent must track not only the physical state but also the possible states of others. This is because others' actions may affect the evolution of the physical state and the agent's payoffs. One approach is to generalize the Bayes filter to multiagent settings, in which an agent tracks the evolution of the interactive state [2]. In practice, the estimation may be carried out using the interactive PF (I-PF) [2] that generalizes the PF to the multiagent setting.

References

[1]
P. Doshi. Improved state estimation in multiagent settings with continuous or large discrete state spaces. In AAAI, pages 712--717, 2007.
[2]
P. Doshi and P. J. Gmytrasiewicz. Approximating state estimation in multiagent settings using particle filters. In AAMAS, pages 320--327, 2005.
[3]
E. Snelson and Z. Ghahramani. Compact approximations to bayesian predictive distributions. In ICML, pages 840--847, 2005.

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  1. Compact approximations of mixture distributions for state estimation in multiagent settings

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    Published In

    cover image Guide Proceedings
    AAMAS '09: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
    May 2009
    730 pages
    ISBN:9780981738178

    Sponsors

    • Drexel University
    • Wiley-Blackwell
    • Microsoft Research: Microsoft Research
    • Whitestein Technologies
    • European Office of Aerospace Research and Development, Air Force Office of Scientific Research, United States Air Force Research Laboratory
    • The Foundation for Intelligent Physical Agents

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    International Foundation for Autonomous Agents and Multiagent Systems

    Richland, SC

    Publication History

    Published: 10 May 2009

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    1. particle filters
    2. representation
    3. state estimation

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