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A Scalable Framework to Choose Sellers in E-Marketplaces Using POMDPs

Athirai Irissappane, Frans A. Oliehoek, and Jie Zhang. A Scalable Framework to Choose Sellers in E-Marketplaces Using POMDPs. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI), pp. 158–164, February 2016.

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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

@inproceedings{Irissappane16AAAI,
    author =    {Athirai Irissappane and Frans A. Oliehoek and Jie Zhang},
    title =     {A Scalable Framework to Choose Sellers in E-Marketplaces Using {POMDPs}},
    booktitle = AAAI16,
    year =      2016,
    month =     feb,
    pages =     {158--164},
    url =       {https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12378/11578},
    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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