Abstract
Low-level individual behavior and higher-level collaborative behavior are presented in a two-level layered architecture for simulated robotic soccer. Shooting to the goal and kicking in a passing situation are achieved by a neural network trained for various positions of the attacker and the goal-keeper or teammate, respectively. As collaboration with teammates passing is considered in various configurations of a group of four attackers and four defenders. Learning the decision of a player: (i) keep ball, (ii) pass ball to closest teammate, (iii) pass ball to medium distance teammate, (iv) pass ball to farthest teammate are studied. The algorithms used to learn this decision-making are OC1 and ITI, for decision tree, and CN2 and RIPPER for rule induction. These results are useful in constructing a decision-maker for a player.
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© 1998 Springer-Verlag Berlin Heidelberg
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Letia, I.A., Joldos, M., Cenan, C., Zaiu, D., Andreica, A. (1998). Decision trees and rule induction in simulated soccer agents. In: Drogoul, A., Tambe, M., Fukuda, T. (eds) Collective Robotics. CRW 1998. Lecture Notes in Computer Science, vol 1456. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033378
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DOI: https://doi.org/10.1007/BFb0033378
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