C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Recognizing Probabilistic Opponent Movement Models
RoboCup 2001: Robot Soccer World Cup V
Automated Assistants to Aid Humans in Understanding Team Behaviors
RoboCup-99: Robot Soccer World Cup III
Using RoboCup in university-level computer science education
Journal on Educational Resources in Computing (JERIC) - Special issue on robotics in undergraduate education. Part 1
Multi-agent strategic modeling in a robotic soccer domain
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Discovering tactical behavior patterns supported by topological structures in soccer agent domains
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Know thine enemy: a champion robocup coach agent
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Learning and multiagent reasoning for autonomous agents
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Recognizing patterns of dynamic behaviors based on multiple relations in soccer robotics domain
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Discovering behavior patterns in multi-agent teams
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
An overview on opponent modeling in RoboCup soccer simulation 2D
Robot Soccer World Cup XV
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The UT Austin Villa 2003 simulated online soccer coach was a first time entry in the RoboCup Coach Competition. In developing the coach, the main research focus was placed on treating advice-giving as a machine learning problem. Competing against a field of mostly hand-coded coaches, the UT Austin Villa coach earned first place in the competition. In this paper, we present the multi-faceted learning strategy that our coach used and examine which aspects contributed most to the coach's success.