• Proceedings of the Korea Information Processing Society Conference
• /
• 2004.05a
• /
• pp.819-822
• /
• 2004

본 논문에서는 가상환경에서의 바둑 게임을 위하여 CyberGlove와 트랙킹 장비를 이용한 사용자 인터페이스를 구축하고 이의 사용성을 평가하였다. 먼저 바둑 게임을 위한 3차원 객체들을 생성한 후, WorldTookit을 이용하여 가상환경을 구축하였다. 그리고 트래킹 리시버가 부착된 CyberGlove를 끼고 바둑함으로부터 "바둑알 집기"와 바둑판 위의 화점에 "바둑알 두기"를 통해 가상환경에 익숙치 않은 사용자들을 대상으로 인터페이스의 사용성을 평가하였다. 실험 결과 현실 세계와 같이 손과 손가락을 움직여 바둑을 두는데 매우 흥미로와 했고, 사용자의 2가지 뷰에 따라 실험한 결과 직교 top 뷰에서의 정확도가 대각 원근 뷰 보다 높게 나타났다. 또한 주변 화점에 바둑알을 둘때의 정확도가 현저히 저하됨을 알 수 있었다. 장기간 사용시 불편함 호소 등 해결해야할 문제가 있음에도 불구하고 가상현실 인터페이스는 보다 몰입적이고 현실감 있는 게임을 위해 필요한 요소임을 확인할 수 있었다.

• The Transactions of the Korea Information Processing Society
• /
• v.3 no.3
• /
• pp.460-470
• /
• 1996

In this paper, we describe the programming methodology which can produce computer Go.After computer Go program with the rules of Go determines a territory for itself, it must evaluate the exact next move. The common design principle of computer Go is to combine such heuristic elements as pattern match, alpha-beta pruning and influence function. In this study, we introduce many other approaches and their results on computer Go, and then show data structures and algorithms to implement computer Go project.

• 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.18 no.5
• /
• pp.77-82
• /
• 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 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.

• Proceedings of the Korean Information Science Society Conference
• /
• 2003.10a
• /
• pp.49-51
• /
• 2003

컴퓨터 바둑에 대한 기존연구에서는 형세를 판단하거나 행마를 하기 위해 게임이론에 입각한 인공지능적 알고리즘을 주로 사용하였다. 본 논문에서는 이러한 틀에서 벗어나 각 착점의 의미를 언어학적으로 파악하고자 하였다. 이를 위해 먼저 바둑 용어를 의미에 따라 구분하여 정리하였고 분류된 용어에 대하여 세분화된 의미특성을 부여하였다. 정리한 용어를 활용하여 각 착점이 반상에서 가지는 역학관계를 표현하고 그 의미를 해석할 수 있게 하였다. 이를 토대로 각 착점의 가치를 수치화할 수도 있게 되었다. 이러한 분석과 분류는 바둑 한판의 내용을 이야기로 보는데 있어서 그 기초를 제공한다.

• Journal of Korea Game Society
• /
• v.20 no.2
• /
• pp.91-100
• /
• 2020

Life-and-death problem in Go is a fundamental problem to be overcome for implementing a computer Go. To solve it, an important consideration is to find out who surrounds or is surrounded between black and white players. To figure out the boundary between black and white groups, we applied an influence function and a contour tracing algorithm. We found that applying the Moore-neighbor tracing among various contour tracing algorithms can create boundaries, and also suggested the possibility of tremendously reducing the search space of a game tree.

• 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.7 no.4
• /
• pp.117-123
• /
• 2007

This paper presents the Stone Influence, the Influence Point, and the Influence Area on computer Go. The Stone Influence is defined using the distance between stone and empty point. The Influence Point is defined using threshold value on the Stone Influence. The Influence Area is defined using lump of the Influence Points and its Core. In experiments using the Jeongseok data, the author obtained the threshold of Influence Points. The proposed method was verified by experiments where it was success fully applied to the influence in game of Go.

• Journal of the Korea Computer Industry Society
• /
• v.3 no.3
• /
• pp.259-270
• /
• 2002

In computer Go, the method evaluating the situation of a face is not generalized. To evaluate the situations all the faces accurately, computer Go must judge owners of 361 positions according the changes of the faces. In this paper, we apply the structure of graph as a method analyzing the rules and characters of Go. The Situation Evaluation System(SES) which can evaluate the situation of a face without DB information oかy using strength of stone(SS), influence power(IP), safety(S), position value(PV), and position-value matrix(PM) is proposed. This system is very effective to evaluate the whole situations of Go because it can show the owner of 361 positions between Black and White. As a result, SES can well compute the situations in the opening game of Go. It makes 70.9% hit-ratio as compared with the practical Go games of professional players. According to the results compared with Nemesis, the commercial program which has the joseki(established stones: hewn sequences of moves near the corner which result in near-equal positions for White and Black), SES is superior to Nemesis by 10% higher in the hit-ratio of situation evaluations of professional players.