• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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• pp.3638-3653
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• 2019

Designing a user interface is important because the user interface determines the level of physical and mental engagement of the user resulting in their level of learning. This paper investigated how physical engagement through a different user interfaces is associated with fun and learning and presented a theoretical physical engagement model called, PEM, developed based on an empirical user study. The PEM model describes how a game user interface is associated with the level of fun and learning, particularly in playing a full body engaged game. There are many different types of games but the Wii Tennis, an embodied interactive game, was chosen as an instance of full body engaged game. A user study with 32 participant's age ranged from 21 to 40 years old revealed that there is a positive correlation between both fun and learning and the level of physical engagement through two different user interfaces. The results of the study showed that the extent of fun and learning are associated with the physical engagement of the player through an interface. As an implication from the study, the result recommend that the level of user engagement is realized by an effective user interface, and the level of physical engagement is determined by the level of authenticity bridged by the user interface.

• 한국게임학회 논문지
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• 제12권1호
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• pp.113-122
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• 2012

본 연구에서는 음악게임을 활용한 음악 감상 교육의 효과 및 만족도를 살펴보았다. 이를 위해 경기도 G초등학교 4학년 남녀학생 107명을 대상으로 실험을 실시하였다. 연구결과 음악 게임을 활용한 수업이 음악 감상을 한 수업보다 학습평가와 학습몰입도에 있어서 높게 나타났고, 특히 만족도에서는 확연히 높은 결과가 나타났다. 또한 학습몰입도와 학습만족도는 학습평가에 영향을 미치는 것으로 분석되었다. 본 연구 결과는 스마트 기기를 학교 정규 수업에 적용하여 학습 콘텐츠 설계 방향을 제시하는데 기여할 것으로 본다.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1074-1082
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• 2020

Recently, research has been actively conducted to create artificial intelligence agents that learn games through reinforcement learning. There are several factors that determine performance when the agent learns a game, but using any of the activation functions is also an important factor. This paper compares and evaluates which activation function gets the best results if the agent learns the game through reinforcement learning in the 2D racing game environment. We built the agent using a reinforcement learning algorithm and a neural network. We evaluated the activation functions in the network by switching them together. We measured the reward, the output of the advantage function, and the output of the loss function while training and testing. As a result of performance evaluation, we found out the best activation function for the agent to learn the game. The difference between the best and the worst was 35.4%.

• 한국게임학회 논문지
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• 제11권5호
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• pp.3-11
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• 2011

게임세대를 위한 효과적인 교육방법으로 게임을 통한 학습이 주목을 받고 있다. 기존 교육용 게임의 설계방법은 필요한 모든 학습요소들과 게임요소들을 동시에 한 게임 안에 수용하도록 설계하는 것이다. 그래서 동기유발과 학습효과를 동시에 달성해야 하는 교육용 게임을 설계하는 것은 일반적으로 어렵다. 교육용게임을 설계하기 어려움의 문제를 해결하기 위해, 본 논문에서는 동기유발기제를 사용하여 게임플레이와 학습플레이를 연동하는 G러닝 모델인 IGLM을 제안하였다. 제안하는 모델 IGLM은 게임레벨들과 학습레벨들을 따로 독립적으로 구성하고 있고 동기유발기제의 연동 메커니즘으로 게임레벨들과 학습레벨들이 서로 연동되어 진행된다. 제안하는 IGLM의 설계방법의 설계 어려움에 대하여 기존 설계방법들과 비교분석하였다. 분석결과에 의하면, 제안하는 IGLM은 기존 방법들의 설계 어려움을 해결할 수 있을 뿐 아니라 구성요소들의 설계의 난이도, 자유도, 그리고 수용범위가 가장 우수한 방법임을 알 수 있었다.

• 한국IT서비스학회지
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• 제16권4호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.3771-3788
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• 2015

This paper studies the distributed energy efficient resource management in the Internet of Things (IoT). Wireless communication networks support the IoT without limitation of distance and location, which significantly impels its development. We study the communication channel and energy management in the wireless communication network supported IoT to improve the ability of connection, communication, share and collaboration, by using the game theory and distributed learning algorithm. First, we formulate an energy efficient neighbor collaborative game model and prove that the proposed game is an exact potential game. Second, we design a distributed energy efficient channel selection learning algorithm to obtain the global optimum in a distributed manner. We prove that the proposed algorithm will asymptotically converge to the global optimum with geometric speed. Finally, we make the simulations to verify the theoretic analysis and the performance of proposed algorithm.

• 디지털산업정보학회논문지
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• 제20권1호
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• pp.59-74
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• 2024

In order to improve safety consciousness due to the increase in disasters and safety accidents, safety education is necessary to prepare for disasters with interest in safety. This study is a board game design for disaster safety education for elementary school students based on Keller's learning motivation theory. By considering the school safety curriculum and the safety education contents of the School Safety Mutual Aid Association and the Ministry of Public Administration and Security, the content and goals of learning were derived and the order of learning was determined. When designing game content, the fun elements of the game were applied to Keller's learning motivation inducing factors such as attention concentration (A), relevance (R), confidence (C), satisfaction (S), and educational game design elements to induce the achievement of learning goals at the game planning stage. It is expected that the existing safety education focusing on lecture-style and audiovisual will be supplemented and used in the educational field.

• 한국경영과학회지
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• 제26권1호
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• pp.61-69
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• 2001

This paper presents a two stage game model which examines the effect of learning-by-doing and spillover. Increases in the firm’s cumulative experience lower its unit cost in future period. However, the firm’s rival also enjoys the experience via spillover. Unlike previous theoretical research model, a cost asymmetric market entry game model is developed between the incumbent firm and new entrant. Mathematical results show that the incumbent firm exploits the learning curve to gain future cost advantage, and that the diffusion of learning to the new entrant induces the incumbent firm to choose decreasing output strategically. As a main result, we show that the relative magnitude between the learning and spillover rate determines the market share ratio of competing firms.

• International Journal of Contents
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• 제16권2호
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• pp.78-91
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• 2020

In this study, an investigation was conducted into the influencing factors for the learning motivation of players in the game, including experience, vicarious experience, the need of achievement, the need of power, and mastery motivation. Then, a discussion was conducted regarding the role played by learning motivation, learning performance, and satisfaction with continuous use. A survey was conducted with 519 players, most at the intermediate gaming level in . As demonstrated by the results of this study, experience, vicarious experience, the need of power, and the mastery of motivation have significant positive association with the players' motivation of learning the game. Learning performance and satisfaction have a positive impact on the continuity of use. Additionally, the correlation between the need of achievement and learning motivation is insignificant. Overall, the research results confirm the significance of the social-cognitive theory relative to the learning motivation. Players began to transform, satisfied with their achievements in the game, as well as gradually evolving toward self-improvement to achieve satisfaction. It offers a new explanation and crucial reference for mastering the gaming trend among the contemporary players.

• 인터넷정보학회논문지
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• 제10권1호
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• pp.93-99
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• 2009

게임프로그램 제작이 단순히 3D 또는 온라인게임 등으로 분류하여 엔진과 게임프로그래밍을 하던 시기를 지나 이제는 게임프로그래밍의 종류를 세분화하여 인공지능 게임프로그래머의 역할이 게임을 좀 더 재미있게 할 수 있는 시점이라 하겠다. 본 논문에서는 강화학습 알고리즘을 이용하여 보상 값을 받아 줄고누 보드게임 말이 학습하게 하여 지능적으로 움직이게 하였다. 구현된 게임 말이 지능적으로 잘 움직이는지 확인하기위해, 보드게임을 제작하여 상대방 말과 승부를 하게 하였다. 실험결과 일정횟수 학습한 이후, 임의로 움직이는 말보다 성능이 월등히 향상됨을 알 수 있었다.