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Active Learning for Player Modeling

Learning models of player behavior has been the focus of several studies. This work is motivated by better understanding of player behavior, a knowledge that can ultimately be employed to provide player-adapted or personalized content. In this paper, we propose the use of active learning for player experience modeling. We use a dataset from hundreds of players playing Infinite Mario Bros. as a case study and we employ the random forest method to learn models of player experience through the active learning approach. The results obtained suggest that only part of the dataset (up to half the size of the full dataset) is necessary for the construction of accurate models that are as accurate as those constructed from the full dataset. This indicates the potential of the method and its benefits in cases when obtaining the data is expensive or time, storage or effort consuming. The results also indicate that the method can be used online during the content generation process where the models can improve and better content can be presented as the game is being played.