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.

翻译：深入理解目标受众（及/或个体）是成功的关键要素——这对视频游戏领域尤为重要且强大，该领域不仅能在开发阶段受益于基于信息的决策，还可理想地在游戏过程中动态调整游戏内容、难度及玩家体验。鉴于人类天性的高度差异性、心理学背景模型的分歧，以及依赖冗长耗时且令人望而生畏的问卷调查这一繁重数据收集方式，对个体性格特质及玩家间差异的精细化评估尤为困难。本研究探索了通过记录的游戏内行为预测一系列玩家个性问卷指标的可能性，并通过在游戏环境中显式添加情感对话决策（此举可能提升模型准确度）来扩展相关工作。采用随机森林回归方法，我们基于七份既定问卷，对62名玩家在定制版角色扮演游戏《辐射：新维加斯》中60分钟游戏行为所对应的多项个性指标进行了预测。研究发现，部分个性变量可通过合理的底层游戏行为与情感表达进行识别，但另一些变量无法预测，或出现无法通过理论背景文献解释的存疑相关性。尽管如此，基于此项探索性研究的初步成果，我们正致力于将数据集大规模扩展至更具生态效度的工业游戏环境中的玩家，并探究更复杂机器学习方法的性能表现。