Neural networks: a systematic introduction
Neural networks: a systematic introduction
Towards collaborative and adversarial learning:: a case study in robotic soccer
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Automated assistants to aid humans in understanding team behaviors
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Introduction to the Theory of Neural Computation
Introduction to the Theory of Neural Computation
Team-Partitioned, Opaque-Transition Reinforced Learning
RoboCup-98: Robot Soccer World Cup II
The CMUnited-99 Champion Simulator Team
RoboCup-99: Robot Soccer World Cup III
RoboCup 2000: Robot Soccer World Cup IV
Interpretation of Spatio-temporal Relations in Real-Time and Dynamic Environments
RoboCup 2001: Robot Soccer World Cup V
RoboCup 2001: Robot Soccer World Cup V
Coaching a Soccer Simulation Team in RoboCup Environment
EurAsia-ICT '02 Proceedings of the First EurAsian Conference on Information and Communication Technology
Recognizing Team Formations in Multiagent Systems: Applications in Robotic Soccer
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Analysis and Forecast of Team Formation in the Simulated Robotic Soccer Domain
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
An overview on opponent modeling in RoboCup soccer simulation 2D
Robot Soccer World Cup XV
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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 field. 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 artificial neural network and will be discussed in this paper. All soccer-clients are initialized with a specific behavior and can change their behavior to an appropriate mode depending on the coach's commands. The result is a flexible and effective game played by the eleven soccer-clients.