A Distributed Branch and Bound Algorithm for Computing Optimal Coalition Structures
Coalition formation is an important area of research in multi agent systems. Computing optimal coalition structures for a large number of agents is an important problem in coalition formation but has received little attention in the literature. Previous studies assume that each coalition value is known a priori. This assumption is impractical in real world settings. Furthermore, the problem of finding coalition values become intractable for even a relatively small number of agents. This work proposes a distributed branch and bound algorithm for computing optimal coalition structures in linear production domain, where each coalition value is not known a priori. The common goal of the agents is to maximize the system's profit. In our algorithm, agents perform two tasks: i) deliberate profitable coalitions, and ii) cooperatively compute optimal coalition structures. We show that our algorithm outperforms exhaustive search in generating optimal coalition structure in terms of elapses time and number of coalition structures generated.
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