• Journal of Korea Game Society
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• v.10 no.4
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• pp.91-101
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• 2010

The purpose of this study is to analyze the teacher's perception about educational use of a game as a supportive method for teaching and learning process in the educational context. The result will be used as a good index to spread the game-based learning in the future. We have derived the following results through the investigation. Firstly, teachers have some limitation to get the topics and contents for game-based learning curriculum. Secondly, they were frequently required to design and arrange their teaching process by the level of learner's ability in the game-based learning. Thirdly, public institution has to supply various information and guideline for teachers to use the game-based learning. Finally, they demand systematic approach and executive and financial support to encourage the game-based learning.

• Journal of Korea Game Society
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• v.20 no.6
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• pp.23-30
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• 2020

There are many cases in the game. So, Game have to learn a lot. This paper uses reinforcement learning to improve the learning speed. However, because reinforcement learning has many cases, it slows down early in learning. So, the speed of learning was improved by using the minimax algorithm. In order to compare the improved performance, a Gonu game was produced and tested. As for the experimental results, the win rate was high, but the result of a tie occurred. The game tree was further explored using progressive deepening to reduce tie cases and win rate has improved by about 75%.

• Journal of Korea Game Society
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• v.18 no.4
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• pp.99-106
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• 2018

Board games have many game characters and many state spaces. Therefore, games must be long learning. This paper used reinforcement learning algorithm. But, there is weakness with reinforcement learning. At the beginning of learning, reinforcement learning has the drawback of slow learning speed. Therefore, we tried to improve the learning speed by using the heuristic using the knowledge of the problem domain considering the game tree when there is the same best value during learning. In order to compare the existing character the improved one. I produced a board game. So I compete with one-sided attacking character. Improved character attacked the opponent's one considering the game tree. As a result of experiment, improved character's capability was improved on learning speed.

• The Journal of Korean Association of Computer Education
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• v.10 no.2
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• pp.65-75
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• 2007

SCORM is an implementation reference model and also a de-facto standard technology designed for developing e-learning contents and systems effectively. For recent years, as many researchers have been more interested than ever in game based learning, ADL as a SCORM developer, has initiated a basic research on game based learning. However, the game based learning research of ADL has been performed conceptually as well as separately from SCORM so that it lacks in efforts for developing a game based learning SCORM content by incorporating concrete game data into SCORM data model. In this paper, we first present a method for applying game data elements to SCORM data Model, and then illustrate a game based learning SCORM content developed by our method.

• 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.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.267-277
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• 2024

This paper explores the use of machine learning in game production to create goal-oriented, realistic animations for skeleton monsters. The purpose of this research is to enhance realism by implementing intelligent movements in monsters within game development. To achieve this, we designed and implemented a learning model for skeleton monsters using reinforcement learning algorithms. During the machine learning process, various reward conditions were established, including the monster's speed, direction, leg movements, and goal contact. The use of configurable joints introduced physical constraints. The experimental method validated performance through seven statistical graphs generated using machine learning methods. The results demonstrated that the developed model allows skeleton monsters to move to their target points efficiently and with natural animation. This paper has implemented a method for creating game monster animations using machine learning, which can be applied in various gaming environments in the future. The year 2024 is expected to bring expanded innovation in the gaming industry. Currently, advancements in technology such as virtual reality, AI, and cloud computing are redefining the sector, providing new experiences and various opportunities. Innovative content optimized for this period is needed to offer new gaming experiences. A high level of interaction and realism, along with the immersion and fun it induces, must be established as the foundation for the environment in which these can be implemented. Recent advancements in AI technology are significantly impacting the gaming industry. By applying many elements necessary for game development, AI can efficiently optimize the game production environment. Through this research, We demonstrate that the application of machine learning to Unity and game engines in game development can contribute to creating more dynamic and realistic game environments. To ensure that VR gaming does not end as a mere craze, we propose new methods in this study to enhance realism and immersion, thereby increasing enjoyment for continuous user engagement.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.1-6
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• 2022

Teaching and learning are one of the major issues during this pandemic (COVID-19). Since the pandemic started, there are many changes in teaching and learning styles as everything related to studies started online. Game-Based Learning has got remarkable importance in the educational system and pedagogy as an effective way of increasing student inspiration and engagement. In this field, most of the work has been carried out in digital games. This research uses an Animated Game-Based Learning design in enhancing student engagement and perception of learning. In teaching Computer Science (CS) concepts in higher education, to enhance the pedagogy activities in CS concepts, more specifically the concepts of "Data Structures (DS)" i.e., Array, Stack, and Queue concepts are focused. This study aims to observe the difference in students' learning with the use of different learning methods i.e., the traditional learning (TL) method and the Animated Game-Based Learning (AGBL) Method. The experimental results show that learning DS concepts has been improved by the AGBL method as compared to the TL method.

• Journal of Korea Game Society
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• v.11 no.3
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• pp.3-8
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• 2011

The learning by computer games, is in the spotlight as an effective education method for the game generation. The educational games need to provide additionally an omniscient view of the learning as an objective viewpoint for storytelling of learning. The omniscient viewpoint needs good readability in real-time, and to provide systematically overall and detailed informations of learning. In this paper, we propose the visual learning map as the omniscient view for storytelling of learning. The proposed learning map is composed of the district map, the topic map, and the progress map for the omniscient view of the learning. The proposed learning map is represented by several visual diagrams for real-time readability. The proposed learning map can apply to all the educational games which provide the game minimaps.

• 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 The Korean Association of Information Education
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• v.20 no.3
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• pp.313-322
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• 2016

The effect on creative problem solving ability and learning flow is analyzed by applying game-based learning using sandbox game, Minecraft Edu for elementary school students. It appeared to be effective when applied to sand box utilizing game-based learning than traditional lecture teaching method on creative problem solving ability and learning flow. It is found to be a significant difference observed in all sub-elements on Creative problem solving ability and it is found to be a significant difference in all sub-elements on learning flow except sense of control and transformation of time.