Abstract:This work explores an unsupervised approach for modelling players of a 2D cube puzzle game with the ultimate goal of customising the game for particular players based solely on their interaction data. To that end, user interactions when solving puzzles are coded as images. Then, a feature embedding is learned for each puzzle with a convolutional network trained to regress the players' completion effort in terms of time and number of clicks. Next, the known bag-of-words technique is used at two levels. First, s… Show more
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