The Intelligent Game Designer: Game Design as a New Domain for Automated Discovery

Designing video games is commonly understood to be a creative task, drawing on a designer’s talent, inspiration, and personal experience. The last ten years have seen multiple calls from the design com-munity to produce reusable knowledge about the structure of games and the design process itself. These designers would like to establish standardized languages and libraries of design patterns so that the next generation of designers can benefit from the best of past generations. The realization of such a move can be read as a transition from thinking about game design as a playable-artifact creation process to a science of play in which we might see the designer’s goal as discovering new gameplay structures and their production of concrete games as a side effect of this process. Thirty years ago, a similar-yet-disconnected thread of research in artificial intelligence was just being born. First marked by Doug Lenat’s AM (an “automated mathematician”), discovery systems aim to automatically produce new and interesting knowledge. Such systems contrast sharply with the then-popular expert systems which applied fixed libraries of “expert ” knowledge to various tasks. Discovery systems, which have commonly operated in the domains of natural science and mathematics, are now seen as distant ancestors of contemporary, statistical machine learning techniques which find extensiv