Sequential decision making in repeated coalition formation under uncertainty

The problem of coalition formation when agents are uncertain about the types or capabilities of their potential partners is a critical one. In [3] a Bayesian reinforcement learning framework is developed for this problem when coalitions are formed (and tasks undertaken) repeatedly: not only does the model allow agents to refine their beliefs about the types of others, but uses value of information to define optimal exploration policies. However, computational approximations in that work are purely myopic. We present novel, non-myopic learning algorithms to approximate the optimal Bayesian solution, providing tractable means to ensure good sequential performance. We evaluate our algorithms in a variety of settings, and show that one, in particular, exhibits consistently good sequential performance. Further, it enables the Bayesian agents to transfer acquired knowledge among different dynamic tasks.