Multi-criteria Decision Making for Local Coordination in Multi-agent Systems

Unlike mono-agent systems, multi-agent planing addresses the problem of resolving conflicts between individual and group interests. In this paper, we are using a Decentralized Vector Valued Markov Decision Process (2V-DEC-MDP) in order to solve this problem. It uses an utility function which is returning a vector representing both individual and group interest. The individual interest of an agent, computed off-line, is based on its optimal policy. The group interest is computed on-line by the agents using their own local observations. In order to take into account both criteria in a decision process and to find a good trade-off between the group interest and the agent´s one, we developed a regret-based algorithm based on the Tchebychev Norm.