Developing a thorough understanding of the target audience (and/or single
individuals) is a key factor for success - which is exceptionally important and
powerful for the domain of video games that can not only benefit from informed
decision making during development, but ideally even tailor game content,
difficulty and player experience while playing. The granular assessment of
individual personality and differences across players is a particularly
difficult endeavor, given the highly variant human nature, disagreement in
psychological background models and because of the effortful data collection
that most often builds upon long, time-consuming and deterrent questionnaires.
In this work, we explore possibilities to predict a series of player
personality questionnaire metrics from recorded in-game behavior and extend
related work by explicitly adding affective dialog decisions to the game
environment which could elevate the model's accuracy. Using random forest
regression, we predicted a wide variety of personality metrics from seven
established questionnaires across 62 players over 60 minute gameplay of a
customized version of the role-playing game Fallout: New Vegas. While some
personality variables could already be identified from reasonable underlying
in-game actions and affective expressions, we did not find ways to predict
others or encountered questionable correlations that could not be justified by
theoretical background literature. Yet, building on the initial opportunities
of this explorative study, we are striving to massively enlarge our data set to
players from an ecologically valid industrial game environment and investigate
the performance of more sophisticated machine learning approaches.

通过分析游戏行为和个性问卷，我们探索了从记录的游戏行为中预测玩家的个人特质问卷指标的可能性，并尝试通过添加情感对话决策来提高模型的准确性。我们使用随机森林回归方法，在定制化的角色扮演游戏《辐射：新维加斯》的 60 分钟游戏时间内，从 62 位玩家的数据中预测了七个已建立的个性问卷的多种指标。尽管一些个性变量可以从合理的游戏行为和情感表达中识别出来，但我们未能找到预测其他变量的方法，也遇到了一些不能通过理论背景文献来解释的可疑相关性。然而，基于这个探索性研究的初步机会，我们计划大规模扩展我们的数据集，研究更复杂的机器学习方法在真实工业游戏环境中的表现。