• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.312-315
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• 2014

The purpose of this study is to develop a system that can be used in the offline replay 'Go(Baduk)' game using the motion detection of image processing techniques. This program will analyse the video of Go(Baduk) game and save the game's data, to help amateur Go(Baduk) players who want to replay their own games easily.

• 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 Korea Game Society
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• v.14 no.6
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• pp.39-48
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• 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
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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.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.24-29
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• 2006

본 연구에서는 기존의 바둑게임을 모바일에 적합한 6면체 표면에서 가능한 게임으로 사용성을 고려하여 구현 하였다. 모바일 버튼의 단순 조작만으로 가능한 게임이다. 바둑이 모바일용으로 대중화 되지 못한 원인인 제한된 스크린에서 가독성의 한계와 게임 소요 시간을 짧게 개선하여 모바일에 적합하도록 하였다. 기존의 평면바둑판을 가로세로 5줄로 주사위형 6면으로 입체화 하였다. 화면 인터페이스는 바둑판의 6면 중 3면이 동시에 보이는 입체도와 6면을 펼친 전개도가 있다. 입체도는 모바일 버튼 조작에 의한 6면을 상하좌우 회전이 가능하도록 하였다. 입체도와 전개도는 동시에 보는 것을 기본으로 하나 사용자 선택에 의해 두 그림의 크기 위치 등이 다양하게 하였다. 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. 시간성, 이동성, 평면에서 입체공간으로의 발상의 전환 등, 모바일 특성을 고려하여 바둑게임을 구현하고자 한다.

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

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.123-131
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• 2016

In this paper, we discussed learner's capability enhancement education suitable for the age of artificial intelligence (AI) using game analysis and archival research based on the 2016 Google Deepmind Challenge match between AI that possessed the finest deep neural networks and the master Baduk player that represented the best of the human minds. AlphaGo was a brilliant move that transcended the conventional wisdom of Baduk and introduced a new paradigm of Baduk. Lee Sedol defeated AlphaGo via the 'divine move and Great idea' that even AlphaGo could not have calculated. This was the triumph of human intuition and insights, which are deeply embedded in human nature as well as human courage and strength. Convergence science education that cultivates student abilities that can help them control machines in the age of AI must be in the direction of developing diverse human insights and positive spirits embedded in human nature not possessed by AI via implementing hearts-on experience and topophilia education obtained from the nature.

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

• International Journal of Contents
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• v.10 no.2
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• pp.37-41
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• 2014

This study defined the pre-elderly as middle age people from 50 to 59. Because it is difficult to produce a design to satisfy the pre-elderly without deeply understanding them, their financial and physical characteristics and persona-based scenario method was studied. An experimental study about persona based scenario method was conducted, and as a result, the types of personas found were as follows: 1) Users enjoy the same games online and offline. 2) Users enjoy playing alone on the computer. 3) Users prefer games that end quickly with win or loss. Writing the situation scenario for each type, the pre-elderly's problems and needs occurring while they play web board games were obtained. The obtained user requests were as follows: users would like the level of difficulty to be simpler in the game of baduk; users wanted unlimited credit and refrainment from using English words in go-Stop; and there were simple comments about game screen design.