Publications• Sorted by Date • Classified by Publication Type • Classified by Research Category • A Scalable Framework to Choose Sellers in E-Marketplaces Using POMDPsAthirai 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. DownloadAbstractIn 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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