Humanization of computational learning in strategy games

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Greenberg, Benjamin S

Advisor dc:contributor.advisor

• Andrew Grant.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/106018

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/106018