Coach planning with opponent models for distributed execution

In multi-agent domains, the generation and coordinated execution of plans in the presence of adversaries is a significant challenge. In our research, a special “coach” agent works with a team of distributed agents. The coach has a global view of the world, but has no actions other than occasionally communicating with the team over a limited bandwidth channel. Our coach is given a set of predefined opponent models which predict future states of the world caused by the opponents’ actions. The coach observes the world state changes resulting from the execution of its team and opponents and selects the best matched opponent model based on its observations. The coach uses the recognized opponent model to predict the behavior of the opponent. Upon opportunities to communicate, the coach generates a plan for the team, using the predictions of the opponent model. The centralized coach generates a plan for distributed execution. We introduce (i) the probabilistic representation and recognition algorithm for the opponent models; (ii) a multi-agent plan representation, Multi-Agent Simple Temporal Networks; and (iii) a plan execution algorithm that allows the robust distributed execution in the presence of noisy perception and actions. The complete approach is implemented in a complex simulated robot soccer environment. We present the contributions as developed in this domain, carefully highlighting their generality along with a series of experiments validating the effectiveness of our coach approach.