In order to demonstrate the viability of the SMS with learning agents and under real-world heavy usage conditions, we ran several tests on the scenario shown in Figure 1. We implemented UIAs that periodically (every 16 seconds) buy a query from some QPA using the protocol described is Section 2. We also used one Auction, AMA, and SCA, along with several UIAs and QPAs. All the agents were deployed in machines all over our network. The UIAs kept track of how long it took for the QPA's reply to arrive and used this value in their learning. In general, the UIAs preferred fast and cheap service, and they were willing to pay more for faster service. A QPA's only preference was to increase its immediate profit, i.e. place its bid in order to maximize its expected profit (remember that failing to get a sale means the QPA gets zero profit).
We gave the agents different learning abilities (see ). 0-level agents used a simple form of reinforcement learning on the prices/values received. 1-level agents actually tried to model the other agents (as 0-level agents) and took actions based on predictions from these models.