Multi-Agent Learning in both Cooperative and Competitive Environments

Our intent is to present a mechanism suitable for agents that, immersed in an environment that is simultaneously cooperative and competitive, have to learn its own best behaviour not only from an individual point of view but also from a global perspective of the system. We consider the learning mechanism we propose to be a multi-agent learning mechanism not only because there are multiple agents learning concurrently in the same environment but also because it allows them to understand how to improve their performance and still not to damage the performance of the other agents. We tested our learning mechanism over the Disruption Management in Airline Operations Control Center application domain and the results show that it provides a good performance to the agents in cooperative as well as in competitive situations in the environment.