Measuring player skill using dynamic difficulty Adjustment

Video games have a long history of use for educational and training purposes, as they provided increased motivation and learning for players. One of the limitations of using video games in this manner is, players still need to be tested outside of the game environment to test their learning outcomes. Traditionally, determining a player’s skill level in a competitive game, requires players to compete directly with each other. Through the application of the Adaptive Training Framework, this work presents a novel method to determine the skill level of the player after each interaction with the video game. This is done by measuring the effort of a Dynamic Diffcult Adjustment agent, without the need for direct competition between players. The experiments conducted in this research show that by measuring the players Heuristic Value Average, we can obtain the same ranking of players as state-of-the-art ranking systems, without the need for direct competition.

Simon Demediuk
Marco Tamassia
William L. Raffe
Fabio Zambetta
Florian 'Floyd' Mueller
Xiaodong Li
Presented At: 
Interactive Entertainment (IE) 2018 - ACSW 2018
Conference Proceedings