IMPLEMENTATION OF A PRE-ASSESSMENT MODULE TO IMPROVE THE INITIAL PLAYER EXPERIENCE USING PREVIOUS GAMING INFORMATION
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.