  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.
