An overview of cooperative and competitive multiagent learning

Multi-agent systems (MASs) is an area of distributed artificial intelligence that emphasizes the joint behaviors of agents with some degree of autonomy and the complexities arising from their interactions. The research on MASs is intensifying, as supported by a growing number of conferences, workshops, and journal papers. In this survey we give an overview of multi-agent learning research in a spectrum of areas, including reinforcement learning, evolutionary computation, game theory, complex systems, agent modeling, and robotics. MASs range in their description from cooperative to being competitive in nature. To muddle the waters, competitive systems can show apparent cooperative behavior, and vice versa. In practice, agents can show a wide range of behaviors in a system, that may either fit the label of cooperative or competitive, depending on the circumstances. In this survey, we discuss current work on cooperative and competitive MASs and aim to make the distinctions and overlap between the two approaches more explicit. Lastly, this paper summarizes the papers of the first International workshop on Learning and Adaptation in MAS (LAMAS) hosted at the fourth International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS’05) and places the work in the above survey.