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Studies on Tactics Searching in a Multi-Agent System

机译:多Agent系统中的策略搜索研究

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摘要

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.
机译:游戏是通过虚拟世界中的经验来理解或研究复杂现象的良好工具之一。现在,计算机代理已开始加入游戏以替代人类玩家。为了帮助通过游戏找到策略,本文提出了一种基于代理的游戏仿真模型。在此模型中,每个代理都有自己的神经网络,用于预测其他代理(包括自身)的行为。另外,每个特工都有一个用于战术决策的分类器模型,为了实现战术目标,特工使用神经网络来获得最佳答案。这些特工试图通过玩针对第二名的游戏来找到战术规则。结果表明,这种三模型结构使我们能够轻松监视代理的行为,并使我们能够考虑游戏世界中的策略。

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