Novel Heuristics for Coalition Structure Generation in Multi-Agent Systems

Abstract

A coalition is a set of self-interested agents that agree to cooperate for achieving a set of goals. Coalition formation is an active area of research in multi-agent systems nowadays. Central to this endeavour is the problem of determining which of the many possible coalitions to form in order to achieve some goal, which is called coalition structure generation. Coalition structure generation problem is extremely challenging due to the number of possible solutions that need to be examined, which grows exponentially with the number of agents involved. Generally, agents would enumerate all possible coalitions, store them in memory, and then try to construct the coalition structure that maximizes the sum of the values of the coalitions. However, this is not feasible when we have a large number of agents, and other constraints on execution time, and memory. Hence, there is a need to develop an algorithm that can generate solutions rapidly for large number of agents while providing bounds on the value of solution as well. With this in mind, we propose two new heuristics, namely LocalSearch and GreedySearch, for generating the coalition structure, which satisfy these properties. We empirically show that these heuristics are able to return ‘good-enough’ solutions in very short time. Furthermore, they enhance the performance of state of the art algorithm, IP (proposed by [12]) in terms of increased lower bound, anytime property, and solution quality. Furthermore, we implemented different heuristics for selecting a sub-space in the IP algorithm and show how the time required to find a good-enough solution depends on the selection of a sub-space in the IP algorithm.

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