Abstract
We have developed a system (SimSoccer Coach) that shows single agent learning by analyzing the fixed opponent’s behavior and then providing offensive and defensive advice to improve the team’s performance. For the offensive advice, the system learns through imitation of successful passing and shooting actions of the opponent’s previous games. For defensive advice, the system learns through observation of the opponent’s passing behavior and thwarts any passing attempts by marking the player and intercepting the ball. To generate these sets of advice, the system reads logfiles of previous games played by a fixed opponent against other teams and selects the data to be used for learning. The C4.5 decision tree algorithm is used to construct the tree and generate production rules based on the selected data. These production rules are converted into CLang advice following the Coach Language grammar. These CLang rules are then given to the coachable team before the game.
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Dulalia, C.L., Go, P.S.L., Tan, P.V.C., Uy, M.Z.I.O., Bulos, R.d.D. (2005). Learning in Coaching. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_136
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DOI: https://doi.org/10.1007/3-540-32391-0_136
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