Modeling Individual Differences in Game Behavior using HMM
This work addresses game design and player engagement by providing a method to model individual differences, though it is incremental as it applies an existing method (HMM) to a new domain in player modeling.
The paper tackled modeling individual differences in player behavior by using Hidden Markov Models (HMM) on sequential in-game actions from a Role-Playing Game, resulting in predictive power for traits like game expertise and conscientiousness, with game expertise being the most influential factor.
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.