Abstract:
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.
