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    Deep Convolutional Player Modelling on Log and Level Data

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    LIAO-UNDERGRADUATERESEARCHOPTIONTHESIS-2018.pdf (1.035Mb)
    Date
    2018-05
    Author
    Liao, Nicholas
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    Abstract
    We present a novel approach to player modeling based on a convolutional neural net trained on game event logs. We test our approach and a hybrid extension over two distinct games, a clone of Super Mario Bros. and Gwario, a human computation version of Super Mario Bros: The Lost Levels. We demonstrate high accuracy in predicting a variety of measures of player experience across these two games. Further we present evidence that our technique derives quality design knowledge and demonstrate the ability to build a more general model.
    URI
    http://hdl.handle.net/1853/60351
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    • School of Interactive Computing Undergraduate Research Option Theses [10]
    • Undergraduate Research Option Theses [862]

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