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22414.pdf (463.16 KB)
ETD Abstract Container
Abstract Header
Adversarial Game Playing Using Monte Carlo Tree Search
Author Info
Sista, Subrahmanya Srivathsava
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479820656701076
Abstract Details
Year and Degree
2016, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Abstract
Monte Carlo methods are a general collection of computational algorithms that obtain results by random sampling. Monte Carlo techniques, while great for simulation, have also found great application in the field of general game playing. We investigate the effectiveness of Monte Carlo methods as applied to general two player games (In this case we use a more interesting variant of the popular game Tic-Tac-Toe: fully observable, deterministic, static, single-agent environment). We set up AI agents, one using Monte Carlo simulation to play and the other using a more traditional mini-max setup. We compare and contrast their performance in all aspects, including efficiency, effectiveness, and cost in terms of memory/processing. After all the data collection and analysis we found that Monte Carlo Techniques tended to perform better relative to the Minimax algorithm when applied to a game of our choice and with restrictive time limits.
Committee
Anca Ralescu, Ph.D. (Committee Chair)
Chia Han, Ph.D. (Committee Member)
Paul G. Talaga, Ph.D. (Committee Member)
Pages
51 p.
Subject Headings
Computer Science
Keywords
monte carlo tree search
;
artificial intelligence
;
game playing
;
tic tac toe
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Citations
Sista, S. S. (2016).
Adversarial Game Playing Using Monte Carlo Tree Search
[Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479820656701076
APA Style (7th edition)
Sista, Subrahmanya Srivathsava.
Adversarial Game Playing Using Monte Carlo Tree Search.
2016. University of Cincinnati, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479820656701076.
MLA Style (8th edition)
Sista, Subrahmanya Srivathsava. "Adversarial Game Playing Using Monte Carlo Tree Search." Master's thesis, University of Cincinnati, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479820656701076
Chicago Manual of Style (17th edition)
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Document number:
ucin1479820656701076
Download Count:
992
Copyright Info
© 2016, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.