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.
Worcester Polytechnic Institute
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Moffett, Jeffrey P., "Applying Causal Models to Dynamic Difficulty Adjustment in Video Games" (2010). Masters Theses (All Theses, All Years). 320.
Causal, Model, Video Game, Causal Mode, Machine Learning