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

The proposed adaptive training framework aims to enhance player proficiency in games and simulations by replacing static difficulty settings with a dynamic system that continuously adjusts to the user's skill level. By utilizing Dynamic Difficulty Adjustment (DDA), the framework seeks to maintain an optimal challenge level, thereby increasing player engagement and accelerating the learning process. The core thesis posits that by monitoring a player’s performance against an adaptive AI opponent and comparing that performance to fixed difficulty benchmarks, it is possible to accurately quantify and track a player’s proficiency over time.

The framework operates through a four-step cycle: the player competes against an adaptive AI, the AI’s performance is evaluated against fixed benchmarks, the player’s proficiency is correlated to the AI’s difficulty, and the AI’s behavior is adjusted to ensure continued growth. A pilot study conducted with two novice players in a custom-built, two-dimensional fighting game tested the efficacy of this approach. Using a fuzzy logic engine, the AI adjusted its aggressiveness based on health differentials between itself and the player. The study successfully demonstrated that the framework could measure player proficiency by comparing the AI's behavior vectors against fixed-difficulty AI benchmarks using cosine similarity.

While the pilot study confirmed the framework's ability to measure proficiency, it did not definitively prove an increased rate of learning, as the sample size and duration were limited. Future iterations of the research intend to incorporate more sophisticated DDA methods, such as Challenge Sensitive Action Selection, and utilize robust ranking systems like TrueSkill to better track progress. The ultimate goal is to move beyond simple health-based metrics to create a scalable, responsive training environment suitable for both entertainment and professional simulation applications.