Theses and Dissertations

Date of Award

5-2020

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Zhixiang Chen

Second Advisor

Dr. Bin Fu

Third Advisor

Dr. Dong-Chul Kim

Abstract

This goal of this thesis is to design and implement a light weighted AI for playing Gomoku with high level intelligence. Our work is built upon an innovative algebraic monomial theory to help assess values for each possible move and estimate chances for the AI to win at each move. With the help of the monomial theory, we are able to convert winning configurations into monomials of variables that represent the underlying board positions. In the existing approaches to building an AI for playing Gomoku, one common challenge is about how to represent the present configuration of the game along with the history of the moves of the two players. Compared with the usual 2D matrix of the board positions, our monomials can make the AI easily understand the current state and the history of the game, and they also allow the AI to compute the potential values for future moves from the current state and the history of moves made by the players. In addition, when we adopt the Monte Carlo Tree Search to probe for a possible winning strategy for the AI, those monomials help reduce the search space, in addition to help estimate rates for exploration of the historical moves and exploitation of the future moves. Based on the proposed algebraic monomial theory, we have implemented a lightweight powerful AI that is capable of playing Gomoku at highly competitive level. At this stage, our AI can win top rated AIs (up to top 7) from the most recent Gomocup rating.

Comments

Copyright 2020 Oscar Garcia. All Rights Reserved.

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