Abstract
Most modern Role-playing video games (RPGs) include an extensive virtual world and interesting narratives that the player can immerse himself in. A captivating and interactive narrative experience is essential in a successful RPG. However, when looking at the options that the player can take to change the virtual environment or the general direction of the narrative, those often feel limited or inconsequential. Furthermore, some content can feel generic and created to please all players, as it opposed to a more personalized experience where the player actions help shape the story. The AAA video game industry have often disregarded this fact and developed very basic Artificial Intelligent and Player Modeling systems that often do not meet the required expectations, despite the fact that these have proven - in academic research - to enhance player interest and expected enjoyment. We do believe that a custom experience, different across player with different traits, motivations and preferences can add more replay value, amusement and better storytelling to any RPG. In this work, we present a Player Modeling architecture that uses a Machine Learning instance that analyses player actions and interactions with the virtual world and associates them with a player profile, in order to create a tailored experience that should provide better enjoyment and immersion for the player. This system was implemented in the popular RPG title The Elder Scrolls V: Skyrim and released as a game modification (mod), which was met with extremely positive feedback by the player community.