Electronic Thesis and Dissertation Repository

Thesis Format

Monograph

Degree

Master of Science

Program

Computer Science

Collaborative Specialization

Artificial Intelligence

Supervisor

Mike Katchabaw

Abstract

The gaming industry has become one of the largest and most profitable industries today. According to market research, the industry revenues will pass $200 Billion and are expected to reach another $20 Billion in 2024. With the industry growing rapidly, players have become more demanding, expecting better content and quality. This means that game studios need new and innovative ways to make their games more enjoyable. One technique used to improve the player experience is DDA (Dynamic Difficulty Adjustment). It leverages the current player state to perform different adjustments during the game to tune the difficulty delivered to the player to be more in line with their expectations and capabilities. In this thesis, we will explore and test the ability to obtain the difficulty level in which a player should be placed initially, by using previous gaming information from platforms like Steam, combined with different machine learning (ML) algorithms and data analyses., In doing so, we can create a pre-assessment of the player as a way of improving DDA’s initial state and the overall gaming experience of players.

Summary for Lay Audience

With the gaming industry growing rapidly, players expect better content and quality. One technique that is being used to improve the player experience is Dynamic Difficulty Adjustment (DDA). DDA systems use the current player data (Health, Score, Damage, etc.) to adjust the difficulty level of the game, making it more in line with their expectations and capabilities. This thesis explores how machine learning (ML) algorithms and data analysis can be used to obtain the initial difficulty level that a player should be placed at, using previous gaming information from platforms like Steam. This pre-assessment can improve DDA's initial adjustment and the overall gaming experience of players.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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