2026-09-19T18:12:59Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1060182026-06-06T00:55:50Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Andrew Grant.
Greenberg, Benjamin S
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-12-22T15:18:46Z
2016-12-22T15:18:46Z
2016
2016
http://hdl.handle.net/1721.1/106018
965829397
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).
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.
by Benjamin S. Greenberg.
M.Eng.
90 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Humanization of computational learning in strategy games
Thesis
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