A scalable framework to choose sellers in E-marketplaces using POMDPs

(2016) A scalable framework to choose sellers in E-marketplaces using POMDPs. .

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© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. 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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Depositing User: Symplectic Admin
Date Deposited: 12 Apr 2016 11:12
Last Modified: 12 Jan 2019 07:23
URI: http://livrepository.liverpool.ac.uk/id/eprint/3000455
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