Modeling perceived difficulty in game levels

The recent interest in procedural content generation for video games has created the need to establish techniques for assessment of generated content. We present an investigation into the factors determining perceived difficulty in procedurally generated game levels. In doing so, an approach to identify relevant factors pertaining to player experience is established, which is subsequently used in the development of predictive difficulty models. In this paper, we apply our methodology to the genre of 2D platformers, presenting an investigation into factors related to difficulty, the development of a test-bed that can be used to collect the data, data collection and subsequent analysis. We investigate the contribution of the identified game and player metrics towards predicting difficulty using Multi-Layer Perceptron, J48 and Random Forest classifiers from WEKA. This work is presented as a preliminary investigation into modeling difficulty from procedural content. Significantly, this investigation provides a preliminary insight into metrics that can be used for developing a classification model for perceived difficulty.