• 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.

• 한국HCI학회:학술대회논문집
• /
• 2006.02a
• /
• pp.830-835
• /
• 2006

본 연구에서는 바둑을 응용하여, 모바일에서 가능한 6면체 게임으로 구현 하였다. 기존의 바둑과 같은 룰과 비슷한 내용의 게임으로서 모바일 버튼의 단순 조작만으로 가능한 게임이다. 게임에 사용되는 6면체 바둑판과 바둑돌들은 PHOSHOP을 이용하여 3차원 모양의 객체로 생성하였다. 프로그래밍은 SKT GENX기반으로 구현하였다. 기존의 바둑판은 가로 세로 교차된19줄이 평면에 그려져 있으나, 6면체 바둑판은 한 평면에 가로 세로 5줄이 주사위 모양의 6표면에 연결되어있다. 대국 시의 모바일 화면 인터페이스는 바둑판의 6면 중 3면을 동시에 보이는 입체도와 6면을 펼친 전개도가 있다. 입체도는 모바일 버튼 조작에 의해 6면을 상하좌우 회전이 가능하도록 하였다. 입체도와 전개도는 동시에 보는 것을 기본으로 하나 사용자 선택에 의해 두 그림의 크기 위치 등이 다양하게 가능하도록 제공하고 있다. 바둑돌의 착점방식은 모바일 버튼 조작에 의해 커서의 이동으로 가능하다. 게임은 네트워크를 이용한 사람과 사람이 가능하도록 약식으로 구현하였다. 게임의 내용은 6면의 입체적 상황을 고려하면서 작전을 세워야 하므로 기존의 평면바둑에 비해 좀더 고난도의 사고와 전략을 요구하는 게임이다. 그리고 6면체 바둑게임은 모바일 스크린환경에서 기존의19줄 평면바둑이 문제시 될 수 있는 가독성과, 한 게임에 사용되는 시간에 대해서 유리한 요인으로 실용화의 가능성을 제시하고자 한다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.10b
• /
• pp.54-57
• /
• 2006

바둑 기보를 보거나 편집하기 위한 소프트웨어들은 대부분 비슷한 인터페이스와 기능을 제공하고 있으며 별다른 성능과 기능이 더 필요하지 않을 만큼 잘 개발되어 있다. 그러나 이런 류의 소프트웨어에서 사용자가 기보를 원하는 방향에서 보기 위해 바둑판을 회전/대칭 처리하고자 할 때 매우 불편함을 경험하게 된다. 그래서 본 연구에서는 이에 대한 직관적인 인터페이스 방법을 제안하고 이를 구현하기 위한 수학적인 연산을 제시하고자 한다. 본 연구에서 제안한 WYSWYG 방식의 인터페이스는 바둑 애호가뿐만 아니라 프로 기사들에게도 매우 편리한 기능이 될 것임에 틀림없다.

• Journal of Korea Game Society
• /
• v.13 no.2
• /
• pp.59-70
• /
• 2013

Although the history of the game of Go is more than 2,500 years, the theoretical studies of Go are still insufficient. In recent years a lot of studies using Artificial Intelligent(AI) have been conducted, but they do not provide the prominent theoretical reality. We applied Principal Component Analysis(PCA) to the professional Go openings, which are the early stage in Go, to analyze them especially focused on the Go game records of the professional 9-dan player Lee Sedol who is the world's top professional Go player. The results showed that among the 361 eigenvectors the 48 most significant eigenvectors capture most of the variance (99.9%) and the 30 most significant eigenvectors enable to possess 90.5 percent of the total variance. This result would be expected to considerably contribute to pattern recognition research of the professional Go openings in the near future.

• The Korean Journal of Applied Statistics
• /
• v.20 no.2
• /
• pp.245-255
• /
• 2007

In playing Baduk, Black plays first and, thus, can control the pace of a game. Usually a player with black stones plays conservatively to maintain the advantage of playing first. The purpose of dum is to compensate for Black having the first move. Currently, 6.5-point dum is applied in Korea and Japan, while 8-point dum is applied in Taiwan and China. In this study we investigated whether the current size of dum(6.5 points) is optimal, by statistically analyzing and comparing the advantage of taking Black across two data sets with different dun rules. Under the 5.5-point handicap, Blacks won significantly more games than Whites, revealing the advantage of playing first. However, with 6.5-point dum, Black's advantage of playing first was not significant. In Conclusion, implications and future research areas are discussed.

• 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.

• Journal of Korea Game Society
• /
• v.13 no.4
• /
• pp.15-24
• /
• 2013

The game of Go, which is originated at least more than 2,500 years ago, is one of the oldest board games in the world. So far the theoretical studies concerning to the Go openings are still insufficient. We applied traditional LDA algorithm to recognize a pro player's opening to a class obtained from the training openings. Both class-independent LDA and class-dependent LDA methods are conducted with the Go game records of the Korean top 10 professional Go players. Experimental result shows that the average recognition rate of class-independent LDA is 14% and class-dependent LDA 12%, respectively. Our research result also shows that in contrary to our common sense the algorithm based on PCA outperforms the algorithm based on LDA and reveals the new fact that the Euclidean distance metric method rarely does not inferior to LDA.

• 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 the Korea Academia-Industrial cooperation Society
• /
• v.9 no.3
• /
• pp.695-701
• /
• 2008

This study has been made to research on the brain wave characteristics of baduk expert by BCI(Brain Computer Interface). The test was based on the researches from 1th September, 2005 to 30th December, 2005, compared with the ones of the standardized general public. The number of the general public are 695 (elementary school students 423, middle and high school students 161, adults 111) and the number of the baduk players are 57 (researchstudents 15, Korean baduk club students 16, professional baduk players 26). The research data show that the baduk players have the higher indexes than the general public in Self Regulation quotient p=.002, Attention Quotient(left) p=.002, Emotion Quotient p=.027, Stress Quotient(left) p=.002 and Brain Quotient p=.006. There are some differences in brain functions between baduk players and the ordinary people. Difference in functions of the brain among baduk experts is also analyzed. That result shows that there is no different brain function between professional baduk player.

• Journal of Korea Game Society
• /
• v.17 no.3
• /
• pp.63-70
• /
• 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.