Exploring Player Trace Segmentation for Dynamic Play Style Prediction

Authors

  • Josep Valls-Vargas Drexel University
  • Santiago Ontañón Drexel University
  • Jichen Zhu Drexel University

Keywords:

player modeling, educational games, sequential machine learning

Abstract

Existing work on player modeling often assumes that the play style of players is static. However, our recent work shows evidence that players regularly change their play style over time. In this paper we propose a novel player modeling framework to capture this change by using episodic information and sequential machine learning techniques. In particular, we experiment with different trace segmentation strategies for play style prediction. We evaluate this new framework on gameplay data gathered from a game-based interactive learning environment. Our results show that sequential machine learning techniques that incorporate predictions from previous segments outperform non-sequential techniques. Our results also show that too fine (minute-by-minute) or too coarse (whole trace) segmentation of traces decreases performance.

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Published

2021-06-24

How to Cite

Valls-Vargas, J., Ontañón, S., & Zhu, J. (2021). Exploring Player Trace Segmentation for Dynamic Play Style Prediction. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 11(1), 93-99. Retrieved from https://ojs.aaai.org/index.php/AIIDE/article/view/12782