Feature Analysis for Modeling Game Content Quality

entertainment for individual game players is to tailor player
experience in real-time via automatic game content generation.
Modeling the relationship between game content and player
preferences or affective states is an important step towards
this type of game personalization. In this paper we analyse the
relationship between level design parameters of platform games
and player experience. We introduce a method to extract the
most useful information about game content from short game
sessions by investigating the size of game session that yields
the highest accuracy in predicting players’ preferences, and by
defining the smallest game session size for which the model
can still predict reported emotion with acceptable accuracy.
Neuroevolutionary preference learning is used to approximate
the function from game content to reported emotional preferences.
The experiments are based on a modified version
of the classic Super Mario Bros game. We investigate two
types of features extracted from game levels; statistical level
design parameters and extracted frequent sequences of level
elements. Results indicate that decreasing the size of the feature
window lowers prediction accuracy, and that the models built
on selected features derived from the whole set of extracted
features (combining the two types of features) outperforms
other models constructed on partial information about game
content.