Modeling Individual Differences in Game Behavior using HMM

TL;DR

This paper introduces a Hidden Markov Model-based approach to capture individual differences in game behavior, enabling classification of player traits like expertise and personality from sequential in-game actions.

Contribution

The study is the first to apply Hidden Markov Models to model individual differences in player behavior and to generate features for trait classification.

Findings

HMM effectively models sequential player behavior.

Game expertise is the most predictive trait.

Some personality traits can be predicted from behavior.

Abstract

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.