Evolutionary Approach to Balance Problem of On-Line Action Role-Playing Game
Evolutionary Approach to Balance Problem of On-Line Action Role-Playing Game
复制标题
在线动作角色扮演游戏平衡问题的进化方法
DOI：
10.1109/wicom.2012.6478518
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
I. Matsuba
中科院分区：
文献类型：
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作者：
Haoyang Chen;Yasukuni Mori;I. Matsuba
采用改进的概率增量程序进化算法(PIPE)和协同协同进化算法(CCEA)相结合的进化体系结构，解决了在线动作角色扮演游戏(ARPG)的均衡问题。我们构建了一个基本在线ARPG的博弈模型，并利用该模型通过蒙特卡罗方法获得了线性Logistic回归模型(LLRM)的训练数据，然后根据LLRM的预测计算个体的适应值。此外，通过一些实验证明，进化体系能够找到一组平衡的能力增长函数(AIF)，这些AIF决定了如何更有效、更合理地为目标游戏增加能力值。
We address the balance problem of on-line Action Role-playing Games(ARPGs) by using an evolutionary architecture which includes intergration with an improved Probabilistic Incremental Program Evolution(PIPE) and Cooperative Coevolutionary Algorithm(CCEA). We construct a game model of the basic on-line ARPG and use it to obtain the training data for a statistical model named Linear Logistic Regression Model(LLRM) by monte carlo method, then we calculate fitness value of the individual based on the prediction of the LLRM. Moreover, some experiments are made to demonstrate the evolutionary architecture is able to find a set of well-balanced Ability-increasing Functions(AIFs), which determine how to increase the ability value with level, for the target game more efficiently and logically.