We are interested in the properties of such MASs. The global behavior of a MAS depends on the behavior of all agents and their interactions.
Agents use a decision policy to select actions based on inputs.
Their goal is to optimize the rewards they get through their reward function which maps changes of the environmental state to scalar reward signals. Since each agent tries to maximize its own rewards, agents behave in their own interests. Therefore conflicts between agents may arise and the need to coordinate the agents emerges. Coordinating agents in a MAS can be done in different ways. We can allow communication between agents, we can create management agencies, or we can have systems in which reward functions are shared among agents.