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

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

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

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.405-408
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• 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
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• v.17 no.3
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• pp.63-70
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• 2017

In AI research Go is regarded as the most challenging board game due to the positional evaluation difficulty and the huge branching factor. MCTS is an exciting breakthrough to overcome these problems. The idea behind AlphaGo was to estimate the winning rate of a given position and then to lead deeper search for finding the best promising move. In this paper, using non-strategic MCTS we verified the fact that most pro players regard the best first move as Tengen (Origin of heaven) in $9{\times}9$ Go is correct. We also compared the average winning rates of the most popular first moves.

• Journal of Korea Game Society
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• v.18 no.5
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• pp.77-82
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• 2018

Go is the most complex board game in which the computer can not search all possible moves using an exhaustive search to find the best one. Prior to AlphaGo, all powerful computer Go programs have used the Monte-Carlo Tree Search (MCTS) to overcome the difficulty in positional evaluation and the very large branching factor in a game tree. In this paper, we tried to find the best sequence of moves using an MCTS on a very small Go board. We found that a $2{\times}2$ Go game would be ended in a tie and the size of Komi should be 0 point; Meanwhile, in a $3{\times}3$ Go Black can always win the game and the size of Komi should be 9 points.

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

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1053-1063
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• 2019

Recently, tile-based streaming that transmits one 360 video in several tiles, is actively being studied in order to transmit these 360 video more efficiently. In this paper, for the transmission of high-definition 360 video corresponding to user's viewport in tile-based streaming scenarios, a system of assigning the quality of tiles at each tile by applying the saliency map generated by existing network models is proposed. As a result of usage of Motion-Constrained Tile Set (MCTS) technique to encode each tile independently, the user's viewport was rendered and tested based on Salient360! dataset, streaming 360 video based on the proposed system results in gain to 23% of the user's viewport compared to using the existing high-efficiency video coding (HEVC).

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.747-759
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• 2018

In this paper, we implement viewport dependent tile partitioning for high quality 360 video transmission and rendering method to present a HMD (Head Mounted Display) screen for 360 video quality evaluation. As a method for high-quality video transmission based on a user's viewport, this paper introduces MCTS (Motion Constrained Tile Sets) technique for solving the motion reference problem and EIS (Extraction Information Sets) SEI including pre-configured tile information, and extractor that extracts tiles. In addition, it explains tile extraction method based on user's viewport and implementation contents of the method of expressing on an HMD. Therefore, if 360 video is transferred by the proposed implementation which only transfers video from the user viewport area, it is possible to express higher quality video with lower bandwidth while avoiding unnecessary image transmission.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.137-148
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• 2017

Researches on the Artificial Intelligence has been explosively activated in various fields since the advent of AlphaGo. Particularly, researchers on the application of multi-layer neural network such as deep learning, and various machine learning algorithms are being focused actively. In this paper, we described a development of an artificial intelligence Janggi game based on reinforcement learning algorithm and MCTS (Monte Carlo Tree Search) algorithm with accumulated game data. The previous artificial intelligence games are mostly developed based on mini-max algorithm, which depends only on the results of the tree search algorithms. They cannot use of the real data from the games experts, nor cannot enhance the performance by learning. In this paper, we suggest our approach to overcome those limitations as follows. First, we collects Janggi expert's game data, which can reflect abundant real game results. Second, we create a graph structure by using the game data, which can remove redundant movement. And third, we apply the reinforcement learning algorithm and MCTS algorithm to select the best next move. In addition, the learned graph is stored by object serialization method to provide continuity of the game. The experiment of this study is done with two different types as follows. First, our system is confronted with other AI based system that is currently being served on the internet. Second, our system confronted with some Janggi experts who have winning records of more than 50%. Experimental results show that the rate of our system is significantly higher.