• KIISE Transactions on Computing Practices
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• v.21 no.8
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• pp.572-577
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• 2015

Generally, the existing Artificial Intelligence (AI) systems were designed for specific purposes and their capabilities handle only specific problems. Alternatively, Artificial General Intelligence can solve new problems as well as those that are already known. Recently, General Video Game Playing the game AI version of General Artificial Intelligence, has garnered a large amount of interest among Game Artificial Intelligence communities. Although video games are the sole concern, the design of a single AI that is capable of playing various video games is not an easy process. In this paper, we propose a GreedyUCB1 algorithm and rollout method that were formulated using the knowledge from a game analysis for the Monte-Carlo Tree Search game AI. An AI that used our method was ranked fourth at the GVG-AI (General Video Game-Artificial Intelligence) competition of the IEEE international conference of CIG (Computational Intelligence in Games) 2014.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.12
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• pp.1657-1664
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• 2018

This paper proposes a decision making scheme for choosing the best move at each state of game in order to implement an artificial intelligence othello game player. The proposed decision making scheme predicts the various possible states of the game when the game has progressed from the current state, evaluates the degree of possibility of winning or losing the game at the states, and searches the best move based on the evaluation. In this paper, we generate learning data by decomposing the records of professional players' real game into states, matching and accumulating winning points to the states, and using the Artificial Neural Network that learned them, we evaluated the value of each predicted state and applied the Minimax search to determine the best move. We implemented an artificial intelligence player of the Othello game by applying the proposed scheme and evaluated the performance of the game player through games with three different artificial intelligence players.

• Journal of Korea Game Society
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• v.20 no.6
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• pp.63-74
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• 2020

Recently, research on automating game tests using artificial intelligence agents instead of humans is attracting attention. This paper aims to collect play data from human and artificial intelligence and analyze their similarity as a preliminary study for game balancing automation. At this time, constraints were added at the learning stage in order to create artificial intelligence that can play similar to humans. Play datas obtained 14 people and 60 artificial intelligence by playing Flippy bird games 10 times each. The collected datas compared and analyzed for movement trajectory, action position, and dead position using the cosine similarity method. As a result of the analysis, an artificial intelligence agent with a similarity of 0.9 or more with humans was found.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.137-148
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• 2017

Researches on the Artificial Intelligence has been explosively activated in various fields since the advent of AlphaGo. Particularly, researchers on the application of multi-layer neural network such as deep learning, and various machine learning algorithms are being focused actively. In this paper, we described a development of an artificial intelligence Janggi game based on reinforcement learning algorithm and MCTS (Monte Carlo Tree Search) algorithm with accumulated game data. The previous artificial intelligence games are mostly developed based on mini-max algorithm, which depends only on the results of the tree search algorithms. They cannot use of the real data from the games experts, nor cannot enhance the performance by learning. In this paper, we suggest our approach to overcome those limitations as follows. First, we collects Janggi expert's game data, which can reflect abundant real game results. Second, we create a graph structure by using the game data, which can remove redundant movement. And third, we apply the reinforcement learning algorithm and MCTS algorithm to select the best next move. In addition, the learned graph is stored by object serialization method to provide continuity of the game. The experiment of this study is done with two different types as follows. First, our system is confronted with other AI based system that is currently being served on the internet. Second, our system confronted with some Janggi experts who have winning records of more than 50%. Experimental results show that the rate of our system is significantly higher.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.93-99
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• 2009

Game program manufacture had been classed by 3D or on-line game etc. simply. But, atomized game programmer's kind now. So, Artificial Intelligence game programmer's role is important. This paper used reinforcement learning algorithm for Jul_Gonu board characters to learn, and so they can move intelligently. To compare a learned character to an random one, a board game was created, and then they fought against each other. As a result, learned character‘s ability was far more improved.

• Korean Journal of Artificial Intelligence
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• v.7 no.2
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• pp.13-17
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• 2019

Currently, the sports market continues to grow every year, and among them, professional baseball's entry income is larger than the rest of the professional league. In sports, strategies are used differently in different situations, and the analysis is based on data to decide which direction to implement. There is a part that a person misses in an analysis, and there is a possibility of a false analysis by subjective judgment. So, if this data analysis is done through artificial intelligence, the objective analysis is possible, and the strategy can be more rationalized, which helps to win the game. The most popular baseball to be applied to artificial intelligence to analyze athletes' strengths and weaknesses and then efficiently establish strategies to ease the competition. The data applied to the experiment were provided on the KBO official website, and the algorithms for forecasting applied linear regression. The results showed that the accuracy was 87%, and the standard error was ±5. Although the results of the experiment were not enough data, it would be possible to effectively use baseball strategies and predict the results of the game if the amount of data and regular data can be applied in the future.

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.91-97
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• 2007

Game program had been classed by 3D or on-line game etc, and engine and game programming simply, But, game programmer's kind more classified new, Artifical Intelligence game programmer's role is important. This paper makes game character study and moved by intelligence using reinforcement learning algorithm. Fought with character enemy using developed game, Confirmed whether embodied game character is facile by intelligence, As result of an experiment, we know, studied character defends excellently than randomly moved character.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1009-1011
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• 2021

게임에서 플레이어에게 시각적으로 제공되는 환경을 View라고 하는데, View의 전환만으로도 전혀 다른 게임의 경험이 가능하다. 본 논문에서는 car racing game에서 View의 전환에 따른 게임의 경험 차이를 비교하였다. 우리는 ARcore 라이브러리를 사용하여 AR car racing game을 구현하였고 virtual joystick을 사용한 Interaction 방법을 구현하였다. Top down view와 first person view의 차이점이 플레이어의 실감에 어떠한 영향을 미치는지 연구하기 위해 두 view을 구현하여 pilot study를 수행하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.145-147
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• 2023

요즘은 모든 산업에서 인공지능(Artificial Intelligence : AI) 채택을 빠르게 진행하고 있으며, 디지털 기술과 산업 기술이 융합된 인공지능 분야가 강화되고 여러 서비스 사업 혁신이 이루어지면서 여러 산업의 시장 성장을 견인하는 것으로 나타났다. 특히 게임 산업과 관련한 게임업계에서는 인공지능 관련 전문 지식을 확보하기 위한 투자가 활발하게 이어짐에 따라 발전과 경쟁력 확보를 위한 움직임들이 지속될 것으로 전망된다. 본 논문에서는 게임개발 기술에 인공지능(AI) 기술 접목이 집중되고 있는 상황에서 개발하고 있는 게임에 대한 품질을 보증하고 관리하기 위한 AI 기반의 게임 QA(Quality Assurance) 기술 적용을 위한 방법들에 대해서 제시하고자 한다.

• Journal of The Korean Association of Information Education
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• v.23 no.3
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• pp.229-235
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• 2019

By combining the elements of the game or game in education, it improves the interest of the students and improves academic achievement by creating an environment where they can participate directly. We propose a curriculum that can learn the core principles of the elementary curriculum through fusion. The proposed curriculum helps students to understand the principles of the elementary curriculum by learning the artificial intelligence method in board game form. Learning methods that incorporate such elements of the game will enable learners to learn the principles of IT so that they can develop their ability to understand objects from various perspectives and enhance their thinking skills. It is expected that the elementary artificial intelligence curriculum that incorporates the proposed gamification will contribute to the development of the information science curriculum, which has been highlighted recently from the 2015 curriculum.