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Cultural Algorithms have been previously employed to model the emergence of cooperative behaviors of agents in different multi-agent systems. In this paper, a simplified and adaptive version will be used as the basis to generate cooperative behaviors within a team of soccer players using different team formations and effective plays. This system can be used as a tutorial for the application of Cultural Algorithms for the coordination of groups of agents in complex multi-agent dynamic environments. Simplified Cultural Algorithms were successful in effectively learning different types of plays, including active and passive protagonists, within a small number of generations. Successful learning includes the coordination of adjustments of the team members to develop the most suitable team formations for every scenario. Experimental results enable us to conclude that Cultural Algorithms, when configured properly, in order to produce significant results, can perform very competitively when compared to other types of learning strategies and case-based game plays.

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