Analysis of Dynamic Difficulty Scaling AI in Video Games

Authors

• Tyrese W. Walker, University of Nebraska - LincolnFollow

First Advisor

Shruti Bolman

Date of this Version

Spring 5-5-2026

Document Type

Thesis

Citation

T. Walker, Analysis of Dynamic Difficulty Scaling AI in Video Games. Undergraduate Honors Thesis, University of Nebraska-Lincoln, 2026. 

Comments

Copyright Tyrese Walker, 2026

Abstract

Video games are a popular form of interactive media and, since their inception, have become one of the largest forms of media consumed. As such, they have evolved greatly from their humble beginnings into much more complex experiences, and adjusting the difficulty level to suit the needs of the player has become common practice. While there are simple ways to do so, the best games often feature a dynamic difficulty-scaling system that adapts to the player. Classics like Resident Evil and Left 4 Dead are excellent examples of innovative dynamic scaling design. By analyzing the strongest elements of these works, we can extrapolate potential new methods of scaling, especially by leveraging advances in machine learning. This paper proposes that a viable next step in dynamic difficulty scaling could come from utilizing probabilistic prediction models to analyze players to inform and adapt enemy non-player character behavior to counter known player strategies.