A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Learning models of other agents using influence diagrams
UM '99 Proceedings of the seventh international conference on User modeling
RoboCup-97: Robot Soccer World Cup I
Role allocation and reallocation in multiagent teams: towards a practical analysis
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Visual recognition of multiagent action
Visual recognition of multiagent action
Finding interesting pass patterns from soccer game records
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
A cost minimization approach to human behavior recognition
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Discovering strategic multi-agent behavior in a robotic soccer domain
Proceedings of the fourth 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
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
Analysis of strategy in robot soccer game
Neurocomputing
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This paper presents an algorithm for multi-agent strategic modeling (MASM) applied in a robotic soccer domain. It transforms a multi-agent action sequence into a set of strategic action descriptions in a graphical and symbolic form. By using hierarchically ordered domain knowledge, the algorithm is able to generate graphic and symbolic strategic action descriptions together with corresponding rules at different levels of abstraction. The method was evaluated on the RoboCup Soccer Server Internet League data.