In a group ball game such as soccer, the ball passing
behavior between players is important for achieving cooperative
team behavior. To acquire the ball passing behavior, conventional
approaches mainly apply search and machine learning to the
decision making of the players who perform the passing action.
On the other hand, the position and posture of the pass receiver
player when receiving the ball have not been studied sufficiently.
This paper proposes a machine learning method using decision
tree based learning to rank to select a more advantageous ball
trapping behavior. We use the RoboCup Soccer Simulator as
an experimental environment to collect training datasets and to
evaluate the performance of the action selection model.
