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