Modeling Individual Differences through Frequent Pattern Mining on Role-Playing Game Actions
Modeling Individual Differences through Frequent Pattern Mining on Role-Playing Game Actions
复制标题
通过角色扮演游戏动作的频繁模式挖掘来建模个体差异
DOI：
10.1609/aiide.v11i5.12847
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
Randy C. Colvin
中科院分区：
文献类型：
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作者：
Zhengxing Chen;M. S. El;Alessandro Canossa;J. Badler;Stefanie Tignor;Randy C. Colvin
已经有很多关于使用收集到的游戏行为数据进行玩家建模的工作。之前许多针对这一目标的研究项目都使用了聚合游戏统计数据作为特征，并使用统计和机器学习技术来开发行为模型。虽然现有的方法已经导致了有趣的发现，但我们怀疑聚合的特征丢弃了有价值的信息，如时间或顺序模式，这些信息在破译有关决策、解决问题或个体差异的信息时可能很重要。这种顺序信息对于分析玩家行为至关重要，尤其是在角色扮演游戏（RPG）中，玩家可以面对大量的选择，体验不同的情境，根据个人倾向自由行动，但最终可能会得到类似的汇总统计数据（如关卡、花费的时间）。在本文中，我们打算开发并应用一种考虑顺序模式的建模技术来破译角色扮演游戏（RPG）游戏中的个体差异。使用具有多种功能的RPG，我们设计了一个实验，收集64名玩家的游戏行为。使用封闭序列模式挖掘和逻辑回归，我们开发了一个模型，该模型使用玩法动作序列来预测现实世界的特征，包括性别、游戏体验和五种人格特征（由心理学定义）。结果表明，游戏经验是影响游戏内行为的主要因素。本文的贡献在于我们开发的算法与验证程序相结合，以确定结果和结果本身的可靠性和有效性。
There has been much work on player modeling using game behavioral data collected. Many of the previous research projects that targeted this goal used aggregate game statistics as features to develop behavior models using both statistical and machine learning techniques. While existing methods have already led to interesting findings, we suspect that aggregated features discard valuable information such as temporal or sequential patterns, which may be important in deciphering information about decisionmaking, problem solving, or individual differences. Such sequential information is critical to analyze player behaviors especially in role-playing games (RPG) where players can face ample choices, experience different contexts, behave freely with individual propensities but possibly end up with similar aggregated statistics (e.g., levels, time spent). In this paper we intend to develop and apply a modeling technique that takes into consideration sequential patters to decipher individual differences in playing a Role Playing Game (RPG) game. Using an RPG with multiple affordances, we designed an experiment collecting granular in-game behaviors of 64 players. Using closed sequential pattern mining and logistic regression, we developed a model that uses gameplay action sequences to predict the real world characteristics, including gender, game play expertise and five personality traits (as defined by psychology). The results show that game expertise is a dominant factor that impacts in-game behaviors. The contribution of this paper is the algorithms we developed combined with a validation procedure to determine the reliability and validity of the results and the results themselves.