Recognizing Formations in Opponent Teams
The online coach within the simulation league has become more powerful over the last few years. Therefore, new options with regard to the recognition of the opponents strategy are possible. For example, the online coach is the only player who gets the inf
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Abstract. The online coach within the simulation league has become more powerful over the last few years. Therefore, new options with regard to the recognition of the opponents strategy are possible. For example, the online coach is the only player who gets the information of all the objects on the £eld. This leads to the idea determine the opponents play system by the online coach and then choose an effective counter-strategy. This has been done with the help of an arti£cial neural network and will be discussed in this paper. All soccer-clients are initialized with a speci£c behavior and can change their behavior to an appropriate mode depending on the coach’s commands. The result is a ¤exible and effective game played by the eleven soccer-clients.
1 Introduction Our team is based on the sources that were released by the CMUnited99 team [10]. We decided to do so because it would have been to time consuming to reinvent all basic skills.1 Instead, we focus on research w.r.t. high level functions which will hopefully lead to new ideas and results for the RoboCup community. Our long run plan is to use part of the provided functions from the CMU-client to construct a more sophisticated team with individual players. Over the last few years several attempts have been made in learning of team behaviour. Similar approaches have been developed from numerous research groups. These studies have the focus on learning team behavior within the simulation and middle size league (see [11], [12], [13], [9]). Raines et al. [7], e.g., describe a new approach to automate assistants to aid humans in understanding team behaviours for the simulation league. This approaches are designed for the analysis of games, off-line after playing, to gain new experiences for the next games. Frank et al. ([4]) present a real time approach which is based on statistical methods. A team will be evaluated statistically but there is no recognition of team strategies. While conducting our research for this project we obtained support from real-life soccer experts. In an interview, Thomas Schaaf, the manager of SV Werder Bremen pointed out the importance of the strategy recognition of the opponent team. While the 1
We would like to give special thanks to the original authors
P. Stone, T. Balch, and G. Kraetzschmar (Eds.): RoboCup 2000, LNAI 2019, pp. 391-396, 2001. c Springer-Verlag Berlin Heidelberg 2001
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Ubbo Visser et al.
coach-client has been able to participate through analysis and control in real matches since 1998 [3], the idea of general strategic planning becomes possible. Like in real life matches, the coach is able to give strategic commands depending on the opponent’s system and the current score. We presume that the performance of our team can be improved by analyzing the opponent’s strategy.
2 Agents The Virtual Werder team consists of individual players which have different behaviors. Players: 22 types of players have been developed with a variety of characteristics to ensure the ¤exibility and variability of actions and reactions wi
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