An empirical study of coaching

In simple terms, one can say that team coaching in adversarial domains consists of providing advice to distributed players to help the team to respond effectively to an adversary. We have been researching this problem to find that creating an autonomous coach is indeed a very challenging and fascinating endeavor. This paper reports on our extensive empirical study of coaching in simulated robotic soccer. We can view our coach as a special agent in our team. However, our coach is also capable of coaching other teams other than our own, as we use a recently developed universal coach language for simulated robotic soccer with a set of predefined primitives. We present three methods that extract models from past games and respond to an ongoing game: (i) formation learning, in which the coach captures a team's formation by analyzing logs of past play; (ii) set-play planning, in which the coach uses a model of the adversary to direct the players to execute a specific plan; (iii) passing rule learning, in which the coach learns clusters in space and conditions that define passing behaviors. We discuss these techniques within the context of experimental results with different teams. We show that the techniques can impact the performance of teams and our results further illustrate the complexity of the coaching problem.