Abstract
Gaming is one of the good tools to understand or study complex phenomena through experiences in a virtual world. Now, computer agents are beginning to join gaming as substitutes for human players. To help finding strategies through a gaming, this paper proposes an agent-based 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 place. It is shown that this three-model structure enables us to monitor behavior of agents easily, and it enables us to consider strategies in the world of gaming.
