Abstract
Games of today are extremely dependent on procedurally generated content to engage players in replaying a game. Most endless-running platformer games tend to generate their content based solely on the duration of the current playthrough, ignoring completely the skill of the player. In order to progress, the player must adapt itself to the game, instead of the game to the player. We propose a model for progression in video games, using the level of mastery of the player when using the different mechanics and overcoming the different challenges provided by the game. The model is used to determine which features (mechanics, challenges) will be presented next to the player in order to maintain the player engaged and in flow. In this dissertation, we implement this model for an endless-running platform game. A custom editor was developed to allow for level designers to specify their own game logic and the rules for guiding the progression of the game as the mastery of the player evolves. The different game features are organised in a graph with conditional transitions specifying the threshold for the level of mastery needed to enable other features down the graph. As the skill of the player evolves, the game will adapt itself in providing features of the appropriate skill level, with the final objective of increasing the level of replayability, fun and engagement thanks to the constant adaption of the game to the skill of the player.