RoboCup: The Robot World Cup Initiative
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Retargetting motion to new characters
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Autonomous behaviors for interactive vehicle animations
Graphical Models - Special issue on SCA 2004
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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.