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Scaling POMDPs For Selecting Sellers in E-markets---Extended Version

Athirai Irissappane, Frans A. Oliehoek, and Jie Zhang. Scaling POMDPs For Selecting Sellers in E-markets---Extended Version. ArXiv e-prints, arXiv:1511.09147, December 2015.

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Abstract

In multiagent e-marketplaces, buying agents need to select good sellers by querying other buyers (called advisors). Partially Observable Markov Decision Processes (POMDPs) have shown to be an effective framework for optimally selecting sellers by selectively querying advisors. However, current solution methods do not scale to hundreds or even tens of agents operating in the e-market. In this paper, we propose the Mixture of POMDP Experts (MOPE) technique, which exploits the inherent structure of trust-based domains, such as the seller selection problem in e-markets, by aggregating the solutions of smaller sub-POMDPs. We propose a number of variants of the MOPE approach that we analyze theoretically and empirically. Experiments show that MOPE can scale up to a hundred agents thereby leveraging the presence of more advisors to significantly improve buyer satisfaction.

BibTeX Entry

@article{Irissappane15arxiv,
    author =    {Athirai Irissappane and Frans A. Oliehoek and Jie Zhang},
    title =     {Scaling {POMDPs} For Selecting Sellers in E-markets---Extended Version},
    journal =   {ArXiv e-prints},
    volume =    {arXiv:1511.09147},
    year =      2015,
    month =     dec,
    abstract = {
    In multiagent e-marketplaces, buying agents need to select good sellers by
    querying other buyers (called advisors). Partially Observable Markov
    Decision Processes (POMDPs) have shown to be an effective framework for
    optimally selecting sellers by selectively querying advisors. However,
    current solution methods do not scale to hundreds or even tens of agents
    operating in the e-market. In this paper, we propose the Mixture of POMDP
    Experts (MOPE) technique, which exploits the inherent structure of
    trust-based domains, such as the seller selection problem in e-markets, by
    aggregating the solutions of smaller sub-POMDPs. We propose a number of
    variants of the MOPE approach that we analyze theoretically and
    empirically. Experiments show that MOPE can scale up to a hundred agents
    thereby leveraging the presence of more advisors to significantly improve
    buyer satisfaction. 
    }
}

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