Adaptive Content Generation for Games

From a simple entertainment activity to a learning tool, it is undeniable that video games are one of the most active and relevant areas in today's society. Since their inception, video games experienced an evolution unmatched by almost any other area and today's video games are complex pieces of software built by expensive multidisciplinary teams. Procedural Content Generation (PCG) offers an alternative to manual design of video game content, optimizing the process of development and thus reducing its cost. However, the content that is generated by traditional PCG techniques is usually very generic and it struggles to offer meaningful game experiences to a diverse player base. Recent years have brought some new PCG techniques that try to solve this problem by dynamically adjusting the generation of content to suit the needs of each individual player. The work presented in this paper focuses on the development of a new PCG methodology that aims to close the gap between game developers and their players. This is achieved by providing the developers with relevant real time player and playing context information and thus creating an easier way for developers to adjust their content generation process in run time to better suit the needs of each player. This was achieved by first designing a methodology that models the player, their context and the game. It was also developed a simple game to showcase the potential of such methodology. The end result was a game that adapts some of its content to different types of players and contexts.