| Summary: | One of the main objectives of playing games is to achieve satisfaction and fun, making the time spent worth it. A digital version is a video game where the user interacts with a computer. However, each player likes to play the game the way they want and at a pace they prefer. This diversity creates different skills levels between different player within the game, making the same difficult not compatible with everyone. If the user cannot identify its playstyle during gameplay, it is likely he will not continue playing the same game in the future. One of the most popular ways to adjust a game to capture the most audience possible and create a positive experience is by creating difficulty levels. Furthermore, this is a manual process and is not a guarantee that it will work since the developers create different difficulties on what they think is the best for most players. A solution to this problem is to automate the process, using state of the art machine learning algorithms that will discover, associate and implement the game parameters that best fit each type of player. The solution proposed is to create an adapted version of a game that can change its content to fit each player personality type. For this, we must first create a profiling system that will determine a player's personality depending on what he feels, acts and behaves during gameplay. Using the information relative to the player and the gameplay, we measure the player's fun and frustration and create an association with the game alterable content. We then alter different game parameters to fit a specific user, ultimately delivering a positive game flow and an optimal game experience. The expected result of the adapted game is that the users have a better experience and game flow. This result can be observed by questionnaires or by the player's actions, for example, longer or more frequent playtimes. Also, adaptivity can help create a more stable player base and improve the game's longevity. Thus, this proposed solution is relevant for any video game that wants to broaden its player base.
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