Coalition formation in multi-agent systems (MAS) is becoming increasingly important as it increases the ability of agents to execute tasks and maximize their payoffs. This is especially true in virtual enterprises, where dynamic coalitions of small, agile enterprises can provide more services and make more profits than an individual can. Moreover, such coalitions can disband when they are no longer effective. Thus the automation of coalition formation will not only save considerable labour time, but also may be more effective at finding beneficial coalitions than human in complex settings.
Coalition formation has been addressed in game theory for some time. However, game theoric approaches are typically centralized and computationally infeasible. MAS researchers, using game theory concepts, have developed algorithms for coalition formation in MAS environments. However, many of them suffer from a number of important drawbacks, for example:
Thus, our research will do a thorough literature review of existing coalition formation algorithms, and evaluate them both theoretically and empirically. Based on our findings, we will develop a more efficient algorithm for coalition formation, applicable for virtual enterprises environment.