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Patrick Riley, Manuela Veloso, and Gal A. Kaminka. An Empirical Study of Coaching. In H. Asama, T. Arai, T. Fukuda, and T. Hasegawa, editors, Distributed Autonomous Robotic Systems 5, pp. 215–224, Springer-Verlag, 2002.
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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.
@InCollection{dars02pat,
author = {Patrick Riley and Manuela Veloso and Gal A. Kaminka},
title = {An Empirical Study of Coaching},
booktitle = {Distributed Autonomous Robotic Systems 5},
publisher = {Springer-Verlag},
pages = {215--224},
year = 2002,
editor = {H. Asama and T. Arai and T. Fukuda and T. Hasegawa},
abstract = {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.},
keywords = {coach, learning for plan recognition, opponent modeling, team training, team analysis }
}
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