Date of Award

Spring 5-18-2018

Degree Type

Thesis

Degree Name

M.S.

Degree Program

Computer Science

Department

Computer Science

Major Professor

Ware, Stephen

Second Advisor

Summa, Christopher

Third Advisor

Hoque, Tamjidul

Abstract

Over the many years since their inception, the complexity of video games has risen considerably. With this increase in complexity comes an increase in the number of possible choices for players and increased difficultly for developers who try to balance the effectiveness of these choices. In this thesis we demonstrate that unsupervised learning can give game developers extra insight into their own games, providing them with a tool that can potentially alert them to problems faster than they would otherwise be able to find. Specifically, we use DBSCAN to look at League of Legends and the metagame players have formed with their choices and attempt to detect when the metagame shifts possibly giving the developer insight into what changes they should affect to achieve a more balanced, fun game.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

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