Humanization of computational learning in strategy games

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-s...

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Bibliographic Details
Main Author: Greenberg, Benjamin S
Other Authors: Andrew Grant.
Format: Others
Language:English
Published: Massachusetts Institute of Technology 2016
Subjects:
Online Access:http://hdl.handle.net/1721.1/106018
Description
Summary: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.