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Gaming is one of the good tools to deal with complex phenomena. Now, computer agents are beginning to join gaming as substitutes for human players. To help designing of a gaming, this paper proposes a model for gaming-simulation. In this model, each agent has its own neural-networks for predicting behavior of other agents, including itself. In addition, each agent has a classifier model for tactical decision-making, and to achieve tactical target, the agent uses neural-networks to get an optimal answer. These agents try to find tactical rules with playing the game that aims at the second phase. It is shown that this three-model structure enables us to monitor behavior of agents easily.