An Adaptive Training Framework for Increasing Player Proficiency in Games and Simulations

To improve a player's proficiency at a particular video game, the player must be presented with an appropriate level of challenge. This level of challenge must remain relative to the player as their proficiency changes. The current fixed difficulty settings (e.g. easy, medium or hard) provide a limited range of difficulty for the player. This work aims to address this problem through developing an adaptive training framework that utilities existing work in Dynamic Difficulty Adjustment to construct an adaptive AI opponent. The framework also provides a way to measure the player's proficiency, by analysing the level of challenge the adaptive AI opponent provides for the player. This work tests part of the proposed adaptive training framework through a pilot study that uses a real-time fighting game.

Simon Demediuk
William L. Raffe
Xiaodong Li
Presented At: 
Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play, Companion Extended Abstracts, pp. 125-131. ACM
Conference Proceedings