Action-Based Digital Characterization of a Game Player

Abstract

Games can be more than just a form of entertainment. Game spaces can be used to test different research ideas quickly, simulate real-life environments, develop non-playable characters (game agents) that interact alongside human players and much more. Game agents are becoming increasingly sophisticated as the collaboration between game agents and humans only continues to grow, and there is an increasing need to better understand game players’ workings. Therefore, this work addresses the digital characterization (DC) of various game players based on the game feature values found in a game space, and based on the actions gathered from player interactions with the game space. High-confidence actions are extracted from rules created with association rule mining, utilizing advanced evolutionary algorithms (e.g., differential evolution) on the dataset of feature values. These high-confidence actions are used in the characterization process, resulting in the DC description of each player. The main research agenda of this study is to determine whether DCs manage to capture the essence of players’ action style behavior. Experiments reveal that characterizations do indeed capture behavior nuances, and consequently open up many research possibilities in the domains of player modeling, analyzing the behavior of different players and automatic policy creation, which can possibly be used for utilization in future simulations.