Exploration/exploitation in stochastic distributed constraint optimization settings

In recent years, important results have been achived in decision-making in uncertain environments, where actions have a direct reward as well as long-term ramifications by bringing in additional information used to improve future decisions. We propose a line of work where this exploration/exploitation tradeoff is applied to distributed settings with interacting independent agents.