• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.54-60
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• 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 Korea Game Society
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• v.21 no.3
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• pp.107-116
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• 2021

Full-board position evaluation in Go is a measurement of judging the advantages and disadvantages between black and white players during a game playing, and through this, the proper tactics and strategies would be undertaken in the near future. In this paper, we tried to evaluate the full-board positions of the 50 AlphaGo vs AlphaGo games using influence function that halved according to the distance. According to the experimental results, there is a limit to making accurate evaluation when the full-board position is assessed only by influence function. In order to overcome this, it is necessary to solve life-and-death problems to deal with dead stones, and it showed that if this is reinforced, we can precisely evaluate the full-board position in Go.

• 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.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 Broadcast Engineering
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• v.22 no.6
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• pp.724-733
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• 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.

• The Journal of Korean Association of Computer Education
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• v.18 no.2
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• pp.21-33
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• 2015

This paper aims at developing IT convergence robot education contents using open hardware-based GoGo Board and presenting three cases that were applied into educational settings with elementary and middle school students. Several types of data for their activities were collected: photos, work output, survey data, video data and interview with robot teacher and students. Each student experienced building up a GoGo Bumper Car with touch sensors attached at front and back sides and figuring out the principle of digital board control and operating of electronic devices by sensing. The participants, in the following phases, conducted domino chain-reaction with GoGo Bumper Cars and acquiring GoGo Driving Licence by driving test on three different road maps. Students in a gifted education program creatively implemented their own ideas as part of robotic art. The result of case analysis showed that the proposed project provides students not only intimacy for technology, fun, concentration but her own empowerment for developing ideas and creative implementation.

• The Journal of Korean Association of Computer Education
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• v.20 no.1
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• pp.49-62
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• 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
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• v.20 no.3
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• pp.45-54
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• 2020

An automated system using a embedded board is required to generate the notation input of the offline Go game. This paper integrates shape and color information of the objects on the Go game board for light-insensitive processing and reduces the computation step. This paper combined the detection of obstacles using connected components with the computation of canny edge detection and HSV-based detection. As a result, the processing time is reduced in the embedded environment so that reliable notation can be automatically stored even in real-time play environment.

• 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.

• International Journal of Contents
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• v.10 no.2
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• pp.74-82
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• 2014

This paper aims to propose the educational concept and design model of a Smart Board Game appropriate for lower elementary grade students. The Smart Board Game refers to a hybrid, or 'convergent', game format based on the offline format of the conventional board game augmented through the use of smart devices. The substantive aspects of this game format utilize the procedural deduction and problem-solving skills applicable in storytelling methodologies and games, in order to generate and propose contents ideal for the educational environment. The development of the Smart Board Game is conducted through the following stages. First, the study analyzes a number of Smart Board Games currently popular in Korea and abroad, based on identification of the key components and processes of the board game format. The subsequent review of the science curriculum for lower elementary grades aims to determine the aspects conducive for application to Smart Board Games. Lastly, the theoretical framework outlined through the previous stages is used as the basis for the proposal of a detailed model of contents for the smart board game concept.