Humanization of computational learning in strategy games
Name
965829397-MIT.pdf
Description
Full printable version
Size
2.64 MB
Format
Adobe PDF
Checksum (MD5)
d95dfc7e90043a4ddaebf367cc5c58ff
Author(s)
Greenberg, Benjamin S
Advisor(s)
Andrew Grant.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
I review and describe 4 popular techniques that computers use to play strategy games: minimax, alpha-beta pruning, Monte Carlo tree search, and neural networks. I then explain why I do not believe that people use any of these techniques to play strategy games. I support this claim by creating a new strategy game, which I call Tarble, that people are able to play at a far higher level than any of the algorithms that I have described. I study how humans with various strategy game backgrounds think about and play Tarble. I then implement 3 players that each emulate how a different level of human players think about and play Tarble.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 89-90).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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