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dc.contributor.advisorRiedl, Mark
dc.contributor.authorLiao, Nicholas
dc.date.accessioned2018-08-20T19:10:44Z
dc.date.available2018-08-20T19:10:44Z
dc.date.created2018-05
dc.date.submittedMay 2018
dc.identifier.urihttp://hdl.handle.net/1853/60351
dc.description.abstractWe 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.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectHuman-centered computing
dc.subjectComputer Games
dc.subjectConvolutional Neural Networks
dc.titleDeep Convolutional Player Modelling on Log and Level Data
dc.typeUndergraduate Research Option Thesis
dc.description.degreeUndergraduate
dc.contributor.departmentInteractive Computing
thesis.degree.levelUndergraduate
dc.contributor.committeeMemberChernova, Sonia
dc.date.updated2018-08-20T19:10:44Z


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