Data-Driven Generation of Simulation Soccer BehaviorsReport
The dynamics of how groups move through space to accomplish common goals must be understood to create realistic synthetic environments. One potential method for creating such multiagent behaviors is to replay prerecorded examples of group movements. While these data-driven methods effectively capture the original performance for a particular instance, the success of these methods for interactive, multiagent applications is limited by the large number of potential agent movements that must be prerecorded. To mitigate the scaling effects of data-driven multiagent behavior algorithms, we propose a behavior model that reduces the dimensionality of prerecorded data and decreases the amount of data required by effectively using available data. We have chosen to investigate the sport of simulated soccer and have developed behaviors for simulated soccer players from the data acquired from recent RoboCup games.
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Brogan, David, and Yannick Loitiere. "Data-Driven Generation of Simulation Soccer Behaviors." University of Virginia Dept. of Computer Science Tech Report (2002).
University of Virginia, Department of Computer Science