Document Type thesis Author Name Moffett, Jeffrey P URN etd-042610-090201 Title Applying Causal Models to Dynamic Difficulty Adjustment in Video Games Degree MS Department Computer Science Advisors Charles Rich, Advisor Joseph Beck, Advisor David Finkel, Reader Michael Gennert, Department Head Keywords Causal Model Video Game Causal Mode Machine Learning Date of Presentation/Defense 2010-04-22 Availability unrestricted
We have developed a causal model of how various aspects of a computer game influence how much a player enjoys the experience, as well as how long the player will play. This model is organized into three layers: a generic layer that applies to any game, a refinement layer for a particular game genre, and an instantiation layer for a specific game. Two experiments using different games were performed to validate the model. The model was used to design and implement a system and API for Dynamic Difficulty Adjustment(DDA). This DDA system and API uses machine learning techniques to make changes to a game in real time in the hopes of improving the experience of the user and making them play longer. A final experiment is presented that shows the effectiveness of the designed system.
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