  Player modeling is an important concept that has gained much attention in
game research due to its utility in developing adaptive techniques to target
better designs for engagement and retention. Previous work has explored
modeling individual differences using machine learning algorithms per- formed
on aggregated game actions. However, players' individual differences may be
better manifested through sequential patterns of the in-game player's actions.
While few works have explored sequential analysis of player data, none have
explored the use of Hidden Markov Models (HMM) to model individual differences,
which is the topic of this paper. In par- ticular, we developed a modeling
approach using data col- lected from players playing a Role-Playing Game (RPG).
Our proposed approach is two fold: 1. We present a Hidden Markov Model (HMM) of
player in-game behaviors to model individual differences, and 2. using the
output of the HMM, we generate behavioral features used to classify real world
players' characteristics, including game expertise and the big five personality
traits. Our results show predictive power for some of personality traits, such
as game expertise and conscientiousness, but the most influential factor was
game expertise.
