A Game Theoretic Framework for Communication in Fully Observable Multiagent Systems

Communication is an important element of multiagent systems (MAS). In fully decentralized systems it is needed to allow the agents to coordinate their actions to achieve certain goals. When the agents have no means to coordinate their actions, they generally choose actions that minimize their chance of losses. If the agents were allowed to coordinate, on the other hand, they can choose actions that allow them to get higher rewards instead. In game theory, these concepts are known as risk dominance and payoff dominance. In this paper, we model communication between agents in stochastic games as an extensive form game in which the agents can numerically evaluate the benefit of communicating a piece of information to the other agents. This allows the agent to answer two important questions about the communication process, namely when should an agent communicate, and what should an agent communicate. We also present a more stable way to select between Nash equilibria in multiagent reinforcement learning using NashQ which is used in the calculation of the values for the communication game.