Bayesian Reinforcement Learning for Coalition Formation under Uncertainty

Abstract

Research on coalition formation usually assumes the values of potential coalitions to be known with certainty. Furthermore, settings in which agents lack sufficient knowledge of the capabilities of potential partners is rarely, if ever, touched upon. We remove these often unrealistic assumptions and propose a model that utilizes Bayesian (multiagent) reinforcement learning in a way that enables coalition participants to reduce their uncertainty regarding coalitional values and the capabilities of others. In addition, we introduce the Bayesian Core, a new stability concept for coalition formation under uncertainty. Preliminary experimental evidence demonstrates the effectiveness of our approach.