Mutual Modeling of Teammate Behavior
نویسندگان
چکیده
In multiagent systems the action that an agent chooses depends on the state of other agents. It is therefore important to have an accurate representation of the current world state. In case the world is only partially observable, an agent does not continually perceive the state of the other agents. It is then essential to model the actions of the other agents to predict their future state. In this paper, we introduce " Mutual Modeling of Teammate Behavior " (MMTB). This model simulates the executed action of a teammate. In combination with the known world dynamics this action can be used to determine the teammate's future state. In this model, we assume that all teammates are homogeneous and follow a policy that is common knowledge among them. Furthermore, we will describe how MMTB is implemented in our RoboCup soccer simulation team UvA Trilearn and give empirical results about its effectiveness.
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