• Journal of Korea Game Society
• /
• v.16 no.4
• /
• pp.79-86
• /
• 2016

Monte-Carlo Tree Search(MCTS) is a best-first tree search algorithm and has been successfully applied to various games, especially to the game of Go. We evaluate the performance of MCTS playing against each other in the game of Tic-Tac-Toe. It reveals that the first player always has an overwhelming advantage to the second player; and we try to find out the reason why the first player is superior to the second player in spite of the fact that the best game result should be a draw. Since MCTS is a statistical algorithm based on the repeated random sampling, it cannot adequately tackle an urgent problem that needs a strategy, especially for the second player. For this, we propose a strategic MCTS(S-MCTS) and show that the S-MCTS player never loses a Tic-Tac-Toe game.

• The Journal of Information Systems
• /
• v.25 no.4
• /
• pp.23-36
• /
• 2016

Purpose Recently, many researchers have paid much attention to the Artificial Intelligence fields of GVGP, PCG. The paper suggests that the improved MCTS algorithm to apply for the framework can generate better AI agent. Design/methodology/approach As noted, the MCTS generate magnificent performance without an advanced training and in turn, fit applying to the field of GVGP which does not need prior knowledge. The improved and modified MCTS shows that the survival rate is increased interestingly and the search can be done in a significant way. The study was done with 2 different sets. Findings The results showed that the 10 training set which was not given any prior knowledge and the other training set which played a role as validation set generated better performance than the existed MCTS algorithm. Besed upon the results, the further study was suggested.

• Journal of Korea Game Society
• /
• v.16 no.6
• /
• pp.79-90
• /
• 2016

Recently, many researchers have paid attention to the improved generation of AI agent in the area of game industry. Monte-Carlo Tree Search(MCTS) is one of the algorithms to search an optimal solution through random search with perfect information, and it is suitable for the purpose of calculating an approximate value to the solution of an equation which cannot be expressed explicitly. Games in Trading Card Game(TCG) genre such as the heartstone has imperfect information because the cards and play of an opponent are not predictable. In this study, MCTS is suggested in imperfect information card games so as to generate AI agents. In addition, the practicality of MCTS algorithm is verified by applying to heartstone game which is currently used.

• Journal of Korea Game Society
• /
• v.18 no.6
• /
• pp.59-68
• /
• 2018

In spite of its simple rule, Go is one of the most complex strategic board games in the field of Artificial Intelligence (AI). Monte-Carlo Tree Search (MCTS) is an algorithm with best-first tree search, and has used to implement computer Go. We try to find the most promising first move using MCTS for playing a Go game on a board of size smaller than $9{\times}9$ Go board. The experimental result reveals that MCTS prefers to place the first move at the center in case of odd-sized Go boards, and at the central in case of even-sized Go boards.

• Journal of Korea Game Society
• /
• v.14 no.3
• /
• pp.47-54
• /
• 2014

The game of Go is one of the oldest games and originated at least more than 2,500 years ago. In game programming the most successful approach is to use game tree searches using evaluation functions. However it is really difficult to construct feasible evaluation function in computer Go. Monte-Carlo Tree Search(MCTS) has created strong computer Go programs such as MoGo and CrazyStone which defeated human Go professionals played on the $9{\times}9$ board. MCTS is based on the winning rate estimated by Monte-Carlo simulation. Prior to implementing MCTS into computer Go, we tried to measure each winning rate of three positions, center, corner and side, in Tic-Tac-Toe playing as the best first move. The experimental result revealed that the center is the best, a corner the next and a side the last as the best first move.

• Journal of Korea Game Society
• /
• v.15 no.5
• /
• pp.109-118
• /
• 2015

The game of Go originated from ancient China is regarded as one of the most difficult challenges in the filed of AI. Over the past few years, the top computer Go programs based on MCTS have surprisingly beaten professional players with handicap. MCTS is an approach that simulates a random sequence of legal moves until the game is ended, and replaced the traditional knowledge-based approach. We applied the UCT algorithm which is a MCTS variant to the game of Tic-Tac-Toe for finding the best first move, and compared it with the result generated by a pure MCTS. Furthermore, we introduced and compared the performances of epsilon-Greedy algorithm and UCB algorithm for solving the Multi-Armed Bandit problem to understand the UCB.

• Smart Media Journal
• /
• v.9 no.4
• /
• pp.36-43
• /
• 2020

The Monte Carlo tree search (MCTS) is a popular method for implementing an intelligent game program. It has several hyper-parameters that require an optimization for showing the best performance. Due to the stochastic nature of the MCTS, the hyper-parameter optimization is difficult to solve. This paper uses the self-playing capability of the MCTS-based game program for optimizing the hyper-parameters. It seeks a winner path over the hyper-parameter space while performing the self-play. The top-q longest winners in the winner path compete for the final winner. The experiment using the 15-15-5 game (Omok in Korean name) showed a promising result.

• Journal of Animal Science and Technology
• /
• v.50 no.1
• /
• pp.45-56
• /
• 2008

In the present study, real-time PCR was performed to evaluated expression of several isoforms of monocarboxylate transporters(MCTs) and two known MCT regulatory proteins, basigin (Bsg) and embigin, in the epididymis of the male reproductive tract during postnatal development. In addition, ERα�-mediated regulation of MCT1 expression in the epididymis was determined with estrogen receptor(ER) α� knockout(α�ERKO) mice by immunohistochemistry. Results from the current study demonstrated differential expression of MCT isoform(MCT 1, 2, 3, 4, and 8), Bsg, and embigin mRNAs in rat epididymis according to postnatal age and epididymal region. In addition, immunohistochemical study of MCT1 revealed the limited localization of MCT1 at apical area of corpus and caudal epididymis. The present study also showed that expression of MCT1 was not directly regulated by ERα�. The findings from the current study suggest that MCTs would involve in establishing adequate microenvironment for sperm maturation and storage in the epididymis, eventually leading to maintenance of male fertility.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.405-408
• /
• 2016

Games like Go, Chess, Janggi have helped to brain development of the people. These games are developed by computer program. And many algorithms have been developed to allow myself to play. The person winning chess program was developed in the 1990s. But game of go is too large number of cases. So it was considered impossible to win professional go player. However, with the use of MCTS(Monte Carlo Tree Search) and CNN(Convolutional Neural Network), the performance of the go algorithm is greatly improved. In this paper, using CNN and MCTS were proceeding development of go algorithm. Using the manual of go learning CNN look for the best position, MCTS calculates the win probability in the game to proceed with simulation. In addition, extract pattern information of go using existing manual of go, plans to improve speed and performance by using it. This method is showed a better performance than general go algorithm. Also if it is receiving sufficient computing power, it seems to be even more improved performance.

• Journal of Korea Game Society
• /
• v.14 no.6
• /
• pp.39-48
• /
• 2014

Go is an extremely complex strategy board game despite its simple rules. By using MCTS, the computer Go programs with handicap game have been defeated human Go professionals. MCTS is based on the winning rate estimated by MC simulation rather than strategy concept. Meanwhile Genetic algorithm equipped with an adequate fitness function can find out the best solutions in the game. The game of Tic-Tac-Toe, also known as Naughts and Crosses, is one of the most popular games. We tried to find out the best strategy in the game of Tic-Tac-Toe. The experimental result showed that Genetic algorithm enables to find efficient strategies and can be applied to other board games such as Go and chess.