Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
RoboCup-97: Robot Soccer World Cup I
Multi-agent strategic modeling in a robotic soccer domain
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Analysis of strategy in robot soccer game
Neurocomputing
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This paper presents a method for multi-agent strategic modeling (MASM) applied in a robotic soccer domain. The method transforms multi-agent action sequences into a visual graph-based diagram, called action graph. Graph nodes are further augmented with additional domain knowledge. Using hierarchical clustering, action graph nodes are merged by utilizing domain-specific distance function. This step results in an abstract graphical model of agent behavior. Then, sub-graphs describing relevant agent behavior are used as input for association rule mining algorithm. The final output of MASM are strategic action concepts in the form of abstract action graph and association rules.