• 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.

• International Journal of Computer Science & Network Security
• /
• v.21 no.6
• /
• pp.54-60
• /
• 2021

Monte-Carlo GO is a computer GO program that is sufficiently competent without using knowledge expressions of IGO. Although it is computationally intensive, the computational complexity can be reduced by properly pruning the IGO game tree. Here, I achieve this by using a potential model based on the knowledge expressions of IGO. The potential model treats GO stones as potentials. A specific potential distribution on the GO board results from a unique arrangement of stones on the board. Pruning using the potential model categorizes legal moves into effective and ineffective moves in accordance with the potential threshold. Here, certain pruning strategies based on potentials and potential gradients are experimentally evaluated. For different-sized boards, including an official-sized board, the effects of pruning strategies are evaluated in terms of their robustness. I successfully demonstrate pruning using a potential model to reduce the computational complexity of GO as well as the robustness of this effect across different-sized boards.

• Journal of Broadcast Engineering
• /
• v.22 no.6
• /
• pp.724-733
• /
• 2017

Conventional methods of automatically recording Go game positions do not properly consider obstacles (hand or object) on a Go board during the Go game. If the Go board is blocked by obstacles, the position of a Go stone may not be correctly recognized, or the sequences of moves may be stored differently from the actual one. In the proposed algorithm, only the complete Go board image without obstacles is stored as a background image and the obstacle is recognized by comparing the background image with the current input image. To eliminate the phenomenon that the shadow is mistaken as obstacles, this paper proposes the new obstacle detection method based on the gradient image instead of the simple differential image. When there is no obstacle on the Go board, the background image is updated. Finally, the successive background images are compared to recognize the position and type of the Go stone. Experimental results show that the proposed algorithm has more than 95% recognition rate in general illumination environment.

• 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.

• 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.

• The Journal of Korean Association of Computer Education
• /
• v.20 no.1
• /
• pp.49-62
• /
• 2017

This paper aims to deeply investigate the interactive patterns between a robot teacher and a participating gifted student who assembled GoGo Bumper car and controlled it with GoGo Monitor commands. Two days of classroom activities for the GoGo Bumper car project were videotaped between July 29 and 30 in 2013. Extra video-based recall interviews were also conducted three times between Nov 2013 and Jan 2014. The qualitative analysis of video data, GoGo Monitor codes and interview data revealed several unfolding patterns of computational thinking. The participating student while interacting with a robot teacher often contemplated and coded on his own ways as he worked with GoGo board testing and assembling a GoGo Bumper car. The overall process of coding and testing his own ideas by finding out relevant commands and arranging them attuned to his computational strategies seems to be cyclic.

• 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.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.

• Proceedings of the Korea Contents Association Conference
• /
• 2017.05a
• /
• pp.95-96
• /
• 2017

최근 현대인들의 다양한 여가생활을 보내는 방법 중의 하나로 관광산업이 급속히 발전되고 있다. 현재 국내 관광산업의 발전을 증진 시킬 수 있는 방안으로 관광객들 사이에 다양한 커뮤니케이션을 유도할 수 있고 국내 관광지 활성화를 이끌어 낼 수 있는 새로운 어플리케이션의 필요성이 대두되고 있다. 따라서 본 논문에서는 국내여행 활성화를 위해 기존 관광관련 어플리케이션과 차별화를 둔 새로운 관광 컨텐츠인 'PicGo' 어플리케이션을 제안하기로 한다. 'PicGo'는 사용자에게 편리함과 즐거움은 더하고, 지도형식의 관광지를 채워가듯 추억을 남기며 회상하게 하는 방식의 UI 디자인을 기반으로 하는 관광만족도에 중점을 둔 어플리케이션이다.