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Q-value Heuristics for Approximate Solutions of Dec-POMDPs

Frans A. Oliehoek and Nikos Vlassis. Q-value Heuristics for Approximate Solutions of Dec-POMDPs. In Proceedings of the AAAI Spring Symposium on Game Theoretic and Decision Theoretic Agents, pp. 31–37, March 2007.

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Abstract

The Dec-POMDP is a model for multi-agent planning under uncertainty that has received increasingly more attention over the recent years. In this work we propose a new heuristic QBG that can be used in various algorithms for Dec-POMDPs and describe differences and similarities with QMDP and QPOMDP. An experimental evaluation shows that, at the price of some computation, QBG gives a consistently tighter upper bound to the maximum value obtainable.

BibTeX Entry

@InProceedings{Oliehoek07GTDTA,
    author =       {Frans A. Oliehoek and Nikos Vlassis},
    title =        {Q-value Heuristics for Approximate Solutions of
                    Dec-{POMDP}s},
    booktitle =    {Proceedings of the {AAAI} Spring Symposium on Game 
                    Theoretic and Decision Theoretic Agents},
    month =        mar,
    year =         2007,
    pages =        {31--37},
    note =	    {},
    abstract = 	 {
    The Dec-POMDP is a model for multi-agent planning under uncertainty
    that has received increasingly more attention over the recent years.
    In this work we propose a new heuristic QBG that can be used in
    various algorithms for Dec-POMDPs and describe differences and 
    similarities with QMDP and QPOMDP. An experimental evaluation 
    shows that, at the price of some computation, QBG gives a 
    consistently tighter upper bound to the maximum value obtainable.}
}

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