Computational Coalition Formation

In many multiagent systems, agents can improve their performance by forming coalitions, i.e., pooling their efforts and resources so as to achieve the tasks at hand in a more efficient way. This holds both for cooperative agents, i.e., agents who share a common set of goals, and for selfish agents who only care about their own payoffs. For cooperative agents, to find the optimal collaboration pattern, it suffices to identify the best way of splitting agents into teams. In contrast, when the agents are selfish, we also have to specify how to distribute the gains from cooperation, since each agent needs to be incentivized to participate in the proposed solution. In this chapter, we discuss coalition formation in multiagent systems for both selfish and cooperative agents. To deal with selfish agents, we introduce classic solution concepts of coalitional game theory that capture the notions of stability and fairness in coalition formation settings. We then give an overview of existing representation formalisms for coalitional games. For each such formalism, we discuss the complexity of computing the solution concepts defined earlier in the chapter, focusing on algorithms whose running time is polynomial in the number of agents n. In the second half of the chapter, we focus on practical approaches for finding an optimal partition of agents into teams. We present the state-of-the-art algorithms for this problem, and compare their relative strengths and weaknesses.