Scaling POMDPs For Selecting Sellers in E-markets-Extended Version
This addresses a scalability bottleneck for multiagent e-marketplaces, enabling more effective seller selection with many advisors, though it is an incremental improvement on existing POMDP methods.
The paper tackles the scalability issue of POMDPs in selecting sellers in e-markets by proposing the Mixture of POMDP Experts (MOPE) technique, which scales up to a hundred agents and significantly improves buyer satisfaction.
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.