Modeling players' behaviors in games has gained increased momentum in the past few years. This area of research has wide applications, including modeling learners and understanding player strategies, to mention a few. In this paper, we present a new methodology, called Interactive Behavior Analytics (IBA), comprised of two visualization systems, a labeling mechanism, and abstraction algorithms that use Dynamic Time Warping and clustering algorithms. The methodology is packaged in a seamless interface to facilitate knowledge discovery from game data. We demonstrate the use of this methodology with data from two multiplayer team-based games: BoomTown, a game developed by Gallup, and DotA 2. The results of this work show the effectiveness of this method in modeling, and developing human-interpretable models of team and individual behavior.
@article{arxiv.2006.11199,
title = {Modeling Individual and Team Behavior through Spatio-temporal Analysis},
author = {Sabbir Ahmad and Andy Bryant and Erica Kleinman and Zhaoqing Teng and Truong-Huy D. Nguyen and Magy Seif El-Nasr},
journal= {arXiv preprint arXiv:2006.11199},
year = {2020}
}