Studies on rule-learning in gaming simulation

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.