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Real-Time Decision Making for Large POMDPs

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Advances in Artificial Intelligence (Canadian AI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3501))

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Abstract

In this paper, we introduce an approach called RTBSS (Real-Time Belief Space Search) for real-time decision making in large POMDPs. The approach is based on a look-ahead search that is applied online each time the agent has to make a decision. RTBSS is particularly interesting for large real-time environments where offline solutions are not applicable because of their complexity.

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Author information

Authors and Affiliations

  1. DAMAS Laboratory, Department of Computer Science and Software Engineering, Laval University,  

    Sébastien Paquet, Ludovic Tobin & Brahim Chaib-draa

Authors
  1. Sébastien Paquet
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  2. Ludovic Tobin
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  3. Brahim Chaib-draa
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Editor information

Editors and Affiliations

  1. Département d’informatique et de recherche opérationelle, CP 6128 succ. Centre-Ville, Université de Montréal, H3C 3J7, Montréal, Canada

    Balázs Kégl

  2. Département d’informatique et de recherche opérationnelle, Université de Montréal,  

    Guy Lapalme

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© 2005 Springer-Verlag Berlin Heidelberg

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Paquet, S., Tobin, L., Chaib-draa, B. (2005). Real-Time Decision Making for Large POMDPs. In: Kégl, B., Lapalme, G. (eds) Advances in Artificial Intelligence. Canadian AI 2005. Lecture Notes in Computer Science(), vol 3501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424918_49

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  • DOI: https://doi.org/10.1007/11424918_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25864-3

  • Online ISBN: 978-3-540-31952-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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