RoboCup-98: Robot Soccer World Cup II
RoboCup-98: Robot Soccer World Cup II
RoboCup-99: Robot Soccer World Cup III
RoboCup-99: Robot Soccer World Cup III
RoboCup-98: Robot Soccer World Cup II
Agilo RoboCuppers: RoboCup Team Description
RoboCup-99: Robot Soccer World Cup III
RoboCup-99: Robot Soccer World Cup III
RoboCup-99: Robot Soccer World Cup III
Communication and Coordination Among Heterogeneous Mid-Size Players: ART99
RoboCup 2000: Robot Soccer World Cup IV
Behavior networks for continuous domains using situation-dependent motivations
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Estimating the absolute position of a mobile robot using position probability grids
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Cooperating Physical Robots: A Lesson in Playing Robotic Soccer
EASSS '01 Selected Tutorial Papers from the 9th ECCAI Advanced Course ACAI 2001 and Agent Link's 3rd European Agent Systems Summer School on Multi-Agent Systems and Applications
RoboCup 2001: Robot Soccer World Cup V
Clockwork Orange: The Dutch RoboSoccer Team
RoboCup 2001: Robot Soccer World Cup V
Evaluation of the Performance of CS Freiburg 1999 and CS Freiburg 2000
RoboCup 2001: Robot Soccer World Cup V
CS Freiburg: Global View by Cooperative Sensing
RoboCup 2001: Robot Soccer World Cup V
Roles, Positionings and Set Plays to Coordinate a RoboCup MSL Team
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Market-based dynamic task allocation using heuristically accelerated reinforcement learning
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Artificial intelligence in robocup
Reasoning, Action and Interaction in AI Theories and Systems
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The success of CS Freiburg at RoboCup 2000 can be attributed to an effective cooperation between players based on sophisticated soccer skills and a robust and accurate self-localization method. In this paper, we present our multiagent coordination approach for both, action and perception, and our rich set of basic skills which allow to respond to a large range of situations in an appropriate way. Furthermore our action selection method based on an extension to behavior networks is described. Results including statistics from CS Freiburg final games at RoboCup 2000 are presented.