Player Knowledge Modeling in Game Design Feedback and Automation

Models that capture the knowledge of players of digital games could be used to great effect in AI-assisted tools that automate or provide feedback for game design. There are several important tasks knowledge models should perform: predicting player performance on a particular task to adjust difficulty, knowing in which order to give particular concepts for maximum learning, or understanding how the pacing of a concept impacts player engagement. While all of these have been explored individual both in games and related fields like intelligent tutoring systems, there have been no models that capture all of these effects together in a way that allows their use in design tools. We propose to expand on previous work in game authoring tools to create tools in which the designer can leverage information about how players learn their game's concepts to create better designs. We will survey the existing player modeling work to find the best representation for this task, deploy these models in adaptive games to learn from data, and then apply these models to create novel game design tools.