Decentralized Decision Making for Multiagent Systems

Decision making in large distributed multiagent systems is a difficult problem. In general, for an agent to make a good decision, it must consider the decisions of all the other agents in the system. This coupling among decision makers has two main causes: (i) the agents share a common objective function (e.g., in a team), or (ii) the agents share constraints (e.g., they must cooperate in sharing a finite resource). The classical approach to this type of problem is to collect all the information from the agents in a single center and solve the resulting optimization problem [see, e.g., Furukawa et al. (2003)]. However, this centralized approach has two main difficulties: