dc.contributor.advisor | Riedl, Mark | |
dc.contributor.author | Liao, Nicholas | |
dc.date.accessioned | 2018-08-20T19:10:44Z | |
dc.date.available | 2018-08-20T19:10:44Z | |
dc.date.created | 2018-05 | |
dc.date.submitted | May 2018 | |
dc.identifier.uri | http://hdl.handle.net/1853/60351 | |
dc.description.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. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_US | |
dc.publisher | Georgia Institute of Technology | |
dc.subject | Human-centered computing | |
dc.subject | Computer Games | |
dc.subject | Convolutional Neural Networks | |
dc.title | Deep Convolutional Player Modelling on Log and Level Data | |
dc.type | Undergraduate Research Option Thesis | |
dc.description.degree | Undergraduate | |
dc.contributor.department | Interactive Computing | |
thesis.degree.level | Undergraduate | |
dc.contributor.committeeMember | Chernova, Sonia | |
dc.date.updated | 2018-08-20T19:10:44Z | |