• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4A
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• pp.373-378
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• 2007

In this paper, the Game Theory based power control for OFDM system is studied, which has attained intensive interest as a core artificial intelligent technology for Cognitive Radio and its efficiency is evaluated using performance metrics such as system throughput and fairness. Utility Function for joint user centric and network centric power control is defined and simulation results show that game theory based power control is far better than closed loop power control. The contribution of this paper is to formalize the game theory based power control toward the Cognitive Radio that recognizes and adapts to the radio communication environments.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.610-618
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• 2021

This thesis is based on the theory of multiple intelligences proposed by the american educator and psychologist Dr.Gardner. According to the definition and classification of children's intelligence development games by predecessors, 6 types of intelligence development suitable for children aged 3 to 6 are summarized games, fill in the questionnaire to understand children's personal preferences, the purpose is to understand whether children aged 3 to 6 have a preference for intelligent development games and whether the preference will be affected by gender and age, and to understand the reality of children aged 3 to 6 Preferences and intellectual development needs provide a factual basis for more scientifically launching intelligent development games.

• Electronics and Telecommunications Trends
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• v.36 no.4
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• pp.81-91
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• 2021

Recently, discussions on Metaverse, which represents the transcendent world, have been dominant for some time. Cases related to the Metaverse are introduced through various media and are continuously attracting attention as the next generation of the Internet. This study reviews the business model and the ecosystem overview, focusing on service cases related to the Metaverse. The widely used business models include content production and sales, media brokerage fee, and marketing fee. The Metaverse ecosystem is formed around games, with major players in game production, authoring tool & support SW, intelligent cloud service, and game platform expected to lead the market. Results show that a strategy to secure the leadership of the Metaverse, such as the business model expansion conditions, a strategy to foster a game-oriented Metaverse ecosystem, and technology development for the realization of the ultra-realistic Metaverse, is necessary.

• Smart Media Journal
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• v.13 no.2
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• pp.32-43
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• 2024

Game agents which are behavioral agent for game playing are a crucial component of game satisfaction. However it takes a lot of time and effort to create game agents for various game levels, environments, and players. In addition, when the game environment changes such as adding contents or updating characters, new game agents need to be developed and the development difficulty gradually increases. And it is important to have a game agent that can be customized for different levels of players. This is because a game agent that can play games of various levels is more useful and can increase the satisfaction of more players than a high-level game agent. In this paper, we propose a method for learning and controlling the level of play of game agents that can be rapidly developed and fine-tuned for various game environments and changes. At this time, reinforcement learning applies a policy-based distributed reinforcement learning method IMPALA for flexible processing and fast learning of various behavioral structures. Once reinforcement learning is complete, we choose actions by sampling based on Softmax-Temperature method. From this result, we show that the game agent's play level decreases as the Temperature value increases. This shows that it is possible to easily control the play level.

• Journal of Korea Multimedia Society
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• v.11 no.1
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• pp.76-87
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• 2008

As hardware performance rises, game users demand higher computer graphic, more convenient UI(User Interface), faster network, and smarter AI(Artificial Intelligence). At this time, however, AI development is accomplished by a co-development team or only one developer. For that reason, it's hard to verify that AI performance and basic game AI technology is lacking for developing high-level AI. Searching the merits and demerits of existing game AI platforms, we investigate main points to consider when designing game AI platforms in this paper. From this we suggest Darwin, a game platform, based on agent that developers embody AI easily and capable of proposing AI test with module that makes them find strategic position. And then evaluate achievement results through making agent used strategic module that Darwin offers.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12A
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• pp.1181-1187
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• 2010

This paper proposes a mobile AI (Artificial Intelligence) conducting decision-making in the game through education for intelligent character on the basis of Neural Network. Neural Network is learned through the input/output value of the algorithm which defines the game rule and the problem solving method. The learned character is able to perceive the circumstances and make proper action. In this paper, the mobile AI using Neural Network has been step-by-step designed, and a simple game has been materialized for its functional experiment. In this game, the goal, the character, and obstacles exist on regular 2D space, and the character, evading obstacles, has to move where the goal is. The mobile AI can achieve its goals in changing environment by learning the solution to several problems through the algorithm defined in each experiment. The defined algorithm and Neural Network are designed to make the input/output system the same. As the experimental results, the suggested mobile AI showed that it could perceive the circumstances to conduct action and to complete its mission. If mobile AI learns the defined algorithm even in the game of complex structure, its Neural Network will be able to show proper results even in the changing environment.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1153-1157
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• 2004

The report of the Korean National Statistics Office shows that Korea has been emerging as an elderly society rapidly, and it will burden the Korean society with excessive social welfare cost for the aged in the near future. If we can help the aged to live healthy in some ways, the social burden for the health care of the aged will be lessened. In order to help physical and mental health of the elderly person, we have developed an exercise apparatus called intelligent arm wrestling machine system. This paper presents the mechanism and scenario of the proposed intelligent arm wrestling machine system. The proposed mechanism and scenario are peculiar. In particular, the proposed scenario determines randomly who will win between the man and the robot and generates a game process that the arm-wrestler cannot predict in advance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.165-168
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• 2005

최근 온라인 게임 시장이 커지면서 게임을 위한 필수요소로 동시 접속자를 지속적으로 확보/유지해야 하는 문제가 이슈화 되고 있다. 온라인 게임을 즐기는 유저 수는 정해져 있는 반면, 게임 컨텐츠 수는 지속적으로 증가되고 있는 상황에서 온라인 게임 시장은 전형적인 레드오션[1]의 시장형태로 변모되어 가고 있다 이러한 문제를 보완하기 위한 대안으로서 본 논문에서는 에이전트 시스템을 이용한 동시 접속자를 생성, 유지할 수 있는 솔루션에 대해서 제시한다. 이를 통해 경쟁력 있는 게임 컨텐츠가 시장 진입을 보다 원활하게 할 수 있을 것으로 기대하며, 아울러 게임 초반의 스트레스 테스트를 위한 툴로써의 활용, 게임의 라이프 사이클의 증대, 경쟁력 강화로 이어질 수 있을 것으로 기대된다

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.195-200
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• 2004

Computer games use an intelligent method called flocking for boids' group behavioral modeling. Flocking can naturally model group behavioral patterns of unpredictable forms such as birds and fishes using some computer resource. In this paper, we implemented an ecosystem which is composed of predator and prey for group behavioral modeling of real underwater ecosystem. Also fuzzy logic is applied to implement instinct desire of ecosystem elements. As the result, we confirmed that the model can overcome breakdown of ecosystem and model naturally ecosystem behavior.