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A Scalable Real-Time Multiagent Decision Making Algorithm with Cost

We focus on a real-time multiagent decision making problem in a collaborative setting including a cost factor for the planning and execution of actions. We present the centralized coordination of a multiagent system in which the team must make a collaborative decision to maximize the global payoff. We used the framework of Coordination Graphs, which exploit dependencies among agents to decompose the global payoff function value as the sum of local terms. We revise the centralized Max-Plus algorithm by presenting a new Cost Max-Plus algorithm for planning and acting by including the cost in the local interactions of agents. We propose a two-step planning and acting algorithm called Factored Value-MCTS-Cost-Max-Plus algorithm that is online, anytime, and scalable in terms of the number of agents and their local interactions.