Towards a Model to meet Players’ Preferences in Games

Different have been the attempts to use Procedural Content Generation via Machine Learning in game development. Among the others, some researchers have tried to adapt a game, or some part of it, to the user playing it. This approach has been called “adaptive game design”. Contrarily to what it may seem, apparently the most interesting findings in this field have been made for drama managers, i.e. for the artificial intelligences that procedurally generate story flow. The paper takes the move from what seems to be a missing in current literature and it is aimed at proposing and discussing a possible procedural content generation via machine learning model that takes the latest approaches in machine learning applied to drama managers and combine them with findings from adaptive game design. The objective of the proposed model is to give players the best possible gaming experience of a highly branched game, depending on their attitudes towards the gaming world.