• Journal of Korea Game Society
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• v.16 no.4
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• pp.79-86
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• 2016

Monte-Carlo Tree Search(MCTS) is a best-first tree search algorithm and has been successfully applied to various games, especially to the game of Go. We evaluate the performance of MCTS playing against each other in the game of Tic-Tac-Toe. It reveals that the first player always has an overwhelming advantage to the second player; and we try to find out the reason why the first player is superior to the second player in spite of the fact that the best game result should be a draw. Since MCTS is a statistical algorithm based on the repeated random sampling, it cannot adequately tackle an urgent problem that needs a strategy, especially for the second player. For this, we propose a strategic MCTS(S-MCTS) and show that the S-MCTS player never loses a Tic-Tac-Toe game.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.2
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• pp.66-75
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• 2010

This paper proposes an efficient method for design and implementation of the artificial intelligence (AI) of 'omok' game on the GPU. The proposed AI is designed on a cooperative structure using min-max game tree and genetic algorithm. Since the evaluation function needs intensive computation but is independently performed on a lot of candidates in the solution space, it is computed on the GPU in a massive parallel way. The implementation on NVIDIA CUDA and the experimental results show that it outperforms significantly over the CPU, in which parallel game tree and genetic algorithm on the GPU runs more than 400 times and 300 times faster than on the CPU. In the proposed cooperative AI, selective search using genetic algorithm is performed subsequently after the full search using game tree to search the solution space more efficiently as well as to avoid the thread overflow. Experimental results show that the proposed algorithm enhances the AI significantly and makes it run within the time limit given by the game's rule.

• Journal of Korea Game Society
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• v.12 no.6
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• pp.95-106
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• 2012

Emotion is very important feature in educational situation. Because it has high influence to memory, educational achievement, motivation. This study tried to find out possibility of serious game as emotional engagement tool in educational situation. We did our pilot experiment to elementary school students who are english as second language. In this L2 learning situation, we did our basic experiment with English language learning serious game called 'Word Collectrian". Word Collectrian has some features for emotional engagement. It has interaction for dynamic word visualization, providing context video for word usage, putting visualized word on learner's virtual home. According to experimental result, word Collectrian has possibility for educational achievement and emotional engagement effect.

• Journal of the Korea Society of Computer and Information
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• v.10 no.3 s.35
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• pp.345-352
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• 2005

This paper aims to design Game Rendering Engine for real-time 3D online games. Previous, in order to raise rendering speed, BSP tree was used to partitioned space in Quake Game Engine. A game engine is required to develop for rapidly escalating of 3D online games in Korea. too. Currently rendering time is saved with the hardware accelerator which is working on the high-level computer system. On the other hand, a game engine is needed to save rendering time for users with low-level computer system. Therefore, a game rendering engine is which reduces rendering time by PVS look-up table using Empty space BSP tree designed and implemented in this paper

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.265-268
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• 2018

컴퓨팅 사고력의 함양을 위해서는 알고리즘을 설계하는 단계가 선행되어야 한다. 그러나 알고리즘 설계와 프로그래밍 구현 활동으로 이루어지는 소프트웨어 교육에서 구체적 조작기에 해당하는 초등학생들에게 추상적인 개념을 필요로 하는 알고리즘은 어려울 수밖에 없다. 학습자들의 흥미를 유도하기 위한 전략 중 하나로 게임 프로그래밍을 활용할 수 있다. 게임 프로그래밍은 또한 문제해결력, 학업성취도, 자기효능감을 향상시킨다. 따라서 본 연구에서는 엔트리와 자연어 알고리즘을 활용하여 초등학생의 컴퓨팅 사고력 향상을 위한 게임 프로그래밍 수업을 설계하고자 한다.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.226-228
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• 2002

본 논문에서는 실시간 대화 행위에서 대화 메시지 사이의 응답 관계를 시각적으로 표현하는 방법을 제안한다. 제안한 방법은 기존 텍스트 기반 대화 방식과 트리 기반 대화 방식을 결합한 형태로서 대화 메시지의 일반적인 응답 관계뿐만 아니라, 기존 트리 기반 인터페이스에서 지원이 어려운 최근 수신 대화 메시지의 응답 관계를 시각화함으로써 대화자의 상호작용을 용이하게 한다. 이러한 방법은 기존 텍스트 방식의 테이블 구조에 트리 구조를 결함하여 대화에서 응답 관계를 명확히 구분한다. 제안한 방법의 구현은 XML과 DOM을 이용하여 대화 메시지와 대화 시스템을 구현하였으며, 응용 분야는 협업, 원격 교육, 온라인 게임 등이다.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.11-14
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• 2002

게임에서 충돌탐지는 게임의 성능향상을 위해 중요하다. 본 논문에서는 효율적인 충돌탐지를 위해 BSP 트리를 사용한다. 공격에 사용되는 스프라이트와 공격의 대상이 되는 스프라이트를 트리로 구성하여 빠른 시간내에 충돌탐지를 행한다. 또한 스프라이트의 모양에 따라 경계 볼륨(bounding volume)을 구와 박스(box)를 선택적으로 사용하여 충돌탐지에서 발생하는 문제점을 해결한다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.706-708
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• 2003

컴퓨터 게임, 지리정보시스템(GIS), 가상현실 분야등에서 환경표현의 기반이 되는 지형의 렌더링 기술은 매우 중요하다. 하지만 지형정보는 일반적으로 방대한 양의 데이터로 구성되어 처리가 쉽지 않다. 지형렌더링을 수행하기 위한 여러가지 기법들 중에 쿼드트리 구조를 기반으로한 연속적 상세단계기법이 있다. 본 논문에서는 쿼드트리 기반의 연속적 상세단계에서 시각 절두체 내에서 렌더링되는 삼각형의 개수를 조절하여 프레임율을 일정하게 유지하는 기법을 제안한다. 이 방법은 인접 프레임간의 일관성을 이용하여 최근 프레임에 가중치를 줌으로써 삼각형의 개수가 급격하게 변하는 것을 막는다.

• 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.