Machine Learning Automation for Virtual Reality


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Virtual Reality (VR) game development techniques are relatively new in relation to conventional 2-dimensional (2D) content. Although there has been significant research conducted in this new field, more work is still needed as there are still some prevalent issues. A significant issue reported by some users is that the perceived difficulty of a game can vary drastically between users. This is because the nature of VR gives more autonomy to users and lets them play games differently than the developer might've intended. To address this, I have proposed a system that tracks user difficulty perception on the manipulation of various game parameters that affect difficulty. The collected user data is used to train a machine learning regressor to predict the perceived difficulty of different game levels. The initial findings show a 53% prediction error. However, further analysis has shown that the predictions are realistic and adequate. Anomalies in prediction are explainable and prediction error can be reduced to 26% through the removal of some outliers. Limitations of this work, like the limited dataset size, are also addressed for future work to improve accuracy and performance. This thesis was primarily written with future work in mind, as the addressed problem is complex and requires further examination for a final and applicable model. The final model proposed uses MCMC optimization and is aimed at automating optimization of game parameters to tailor experiences to intended difficulty and/or emotions. Thus, the main contribution of this paper is its address of an insufficiently covered issue by producing a key approach and proposing detailed suggestions for future research.