Probabilistic decision making in robot soccer

Abstract

Decision making is an important issue in robot soccer, which has not been investigated deeply enough by the RoboCup research community. This paper proposes a probabilistic approach to decision making. The proposed methodology is based on the maximization of a game situation score function, which generalizes the concept of accomplishing different game objectives as: passing, scoring a goal, clearing the ball, etc. The methodology includes a quantitative method for evaluating the game situation score. Experimental results in a high-level strategy simulator, which runs our four-legged code in simulated AIBOs' robots, show a noticeable improvement in the scoring effectiveness achieved by a team that uses the proposed approach for making decisions. © 2008 Springer-Verlag Berlin Heidelberg.