Faculty Advisor

Sarkozy, Gabor N

Faculty Advisor

Selkow, Stanley M.

Center

Budapest, Hungary

Abstract

This paper implements and analyzes four algorithms for improving computer play of the board game Go. These algorithms use machine pattern learning to find better Monte-Carlo simulation policies for use with Monte-Carlo Tree Search. Two of these algorithms maximize individual move strength, and two minimize overall simulation error. These algorithms are tested using UCT on 9x9 Go with 3x3 patterns.

Publisher

Worcester Polytechnic Institute

Date Accepted

April 2009

Major

Computer Science

Project Type

Major Qualifying Project

Accessibility

Unrestricted

Advisor Department

Computer Science

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