Learning Cooperation In Repeated Games
Michael Rovatsos, Jürgen Lind · 1999
. In the field of multi-agent systems, the study of coordination, cooperation and collaboration assumes a prominent position. Most of the research concerned with these issues concentrates on explicit negotiation between agents, on the investigation of settings in which global system goals have to be balanced with agents' individual goals or on the exploitation of real-world knowledge to determine efficient coordination strategies. We present a social learning and reasoning component as part of a layered learning agent architecture for iterated multi-player games, which is capable of implementing cooperative behaviour in societies of purely "selfish" agents. This can be accomplished by learning about other agents' preferences, and by finding out how valuable other agents' actions are for the agent's own success. We claim that this is possible without any a priori knowledge of the underlying payoff matrices and without explicit communication between agents, and first experiments yield promising results. 1