Gal A. Kaminka: Publications

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An Empirical Study of Coaching

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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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.

BibTeX

@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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