# Modeling Individual Differences in Game Behavior using HMM

arXiv: https://arxiv.org/abs/1804.00245 · HTML: https://mlsanity.com/paper/1804.00245/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2018.

## Built on by (most cited first, 5 of 5)

- [Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2](https://mlsanity.com/paper/2210.09028/) (2022)
- [Personalized Game Difficulty Prediction Using Factorization Machines](https://mlsanity.com/paper/2209.13495/) (2022)
- ["All of Me": Mining Users' Attributes from their Public Spotify Playlists](https://mlsanity.com/paper/2401.14296/) (2024)
- [Modeling Player Personality Factors from In-Game Behavior and Affective Expression](https://mlsanity.com/paper/2308.14224/) (2023)
- [Beyond Asking: A Pipeline for Personalized Game Generation that Reads Players from Behavior](https://mlsanity.com/paper/2608.16196/) (2026)

## Abstract (arXiv)

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.