In the RoboCup soccer simulation 2D league, players make a decision at each cycle in real time. The performance of a team highly depends on the agents’ decision-making process, which is composed of a action planning method and an evaluation function of the soccer field. In this work, a cooperative action planning based on the tree search is employed. Each action is evaluated by an evaluation function. We employ a multi-layered perceptron to construct an evaluation function. We examine the performance of the soccer agents when various sets of features are used as the input of the neural network. A feature vector is made of kick sequences executed by an expert team extracted from log files. To investigate the efficiency of our approach, we compare the performance of a team using an evaluation function modeled by neural networks against a team using a hand-tuned evaluation function.
