Computer games provide an ideal test bed to collect and study data related to human behavior using a virtual environment having real-world-like features. Studies regarding individual players’ actions in a gaming session and how this correlates with their real-life personality have the potential to reveal great insights in the field of affective computing. This study profiles players using data collected from strategy games. This is done by taking into account the gameplay and the associations between the personality traits and the subjects playing the game. This study uses two benchmark strategy game datasets, namely, StarCraft and World of Warcraft. In addition, the study also uses the Age of Empire-II game data, collected using 50 participants. The IPIP-NEO-120 personality test is conducted using these participants to evaluate them on the Big-Five personality traits. The three datasets are profiled using four clustering techniques. The results identify two clusters in each of these datasets. The quality of cluster formation is also evaluated through the cluster evaluation indices. Using the clustering results, the classifiers are then trained to classify a player, after a gameplay, into one of the two profiles. Results show that the gameplay can be used to predict various personality features using strategy game data.

中文翻译：

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使用真实世界数据集分析玩家：对数据进行聚类并将结果与​​五大人格特征相关联

电脑游戏提供了一个理想的测试平台，可以使用具有真实世界特征的虚拟环境来收集和研究与人类行为相关的数据。关于单个玩家在游戏中的行为以及这与他们现实生活中的个性如何相关的研究有可能揭示情感计算领域的深刻见解。这项研究使用从策略游戏中收集的数据来描述玩家。这是通过考虑游戏玩法以及人格特征与玩游戏的主题之间的关联来完成的。本研究使用两个基准策略游戏数据集，即星际争霸和魔兽世界。此外，该研究还使用了帝国时代-II 游戏数据，该数据由 50 名参与者收集。IPIP-NEO-120 性格测试是使用这些参与者进行的，以评估他们的大五人格特质。这三个数据集使用四种聚类技术进行分析。结果在每个数据集中确定了两个集群。集群形成的质量也通过集群评价指标进行评价。使用聚类结果，然后训练分类器以在游戏后将玩家分类为两个配置文件之一。结果表明，游戏玩法可用于使用策略游戏数据预测各种个性特征。然后训练分类器以在游戏后将玩家分类为两个配置文件之一。结果表明，游戏玩法可用于使用策略游戏数据预测各种个性特征。然后训练分类器以在游戏后将玩家分类为两个配置文件之一。结果表明，游戏玩法可用于使用策略游戏数据预测各种个性特征。