• Journal of Korea Game Society
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• v.8 no.1
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• pp.91-101
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• 2008

Most designers use professional 3D animation tools such as 3DS MAX to manually create animation. This manual method requires a great deal of time and efforts, and does not allow animation characters to interact with one another. In this paper, we design an AI middleware of form as 3DS MAX plug-in to solve these issues. We present an AI expression structure and internal processing method for this middleware, and the method for creating AI character's structure. It creates AI character's structure by drawing figures and lines for representing AI elements. For experiment, we have produced same animations with the traditional method and our method, and measured the task volume in both methods. This result verifies that the task volume is similar or higher than the traditional method in small-scale tasks, but up to 43% of the task volume is reduced in large-scale tasks. Using the method proposed in this paper, we see that characters in an animation interact each other, and task volume in large-scale tasks are reduced.

• Journal of Digital Contents Society
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• v.7 no.1
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• pp.35-40
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• 2006

Game AI(Artificial Intelligence) technologies implement the movement of autonomous characters and control the movement of Non-Player Characters(NPC). In this paper, we present several types of game AI control systems such as Movement Scripts, FSM(Finite State Machines), Hierarchical State Machines, fuzzy State Machines, and Pathfinding techniques and their limitations.

• International Journal of Contents
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• v.2 no.3
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• pp.18-21
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• 2006

Nowadays, many people enjoy playing games in computer. In this kind of game, people often meet NPC (Non Player Character). It is the virtual character in simplified form of real player and exits in most of current computer games. Various NPCs add the reality and atmosphere of the game as well as help players. There are several techniques to embody NPC, but developers generally use AI technique. This paper discusses some artificial intelligence techniques used in game contents. Especially this paper focuses on the AI techniques used in computer games in terms of the two main approaches, symbolic approach and sub-symbolic approach.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.333-334
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• 2022

본 논문에서는 대전 격투 게임에서의 AI 개발을 위한 강화학습 사용 방법을 제안한다. 이 방법은 학습 모델에 상대방의 다양한 패턴을 학습시켜 적은 코드로 효율적인 AI 개발을 할 수 있어 개발 시간을 최소화 할 수 있다. 또한, 이 방법은 복잡한 코드를 추가 또는 제거할 필요 없이 보상과 액션을 조정하여 다양한 종류의 AI를 원하는 만큼 생성할 수 있다는 장점이 있다. 본 논문에서는 Unity 사에서 제공하는 머신러닝 툴인 ML-Agents를 활용하여 강화학습을 통한 대전 격투 게임 AI의 가능성을 보인다.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.95-111
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• 2021

Despite the innovation of AI and Big Data, theoretical research bout the effect of AI and Big Data on market competition is still in early stages; therefore, this paper analyzes the effect of AI, Big Data, and data sharing on an entry firm by using game theory. In detail, the firms' business environments are divided into internal and external ones. Then, AI algorithms are divided into algorithms for (1) customer marketing, (2) cost reduction without automation, and (3) cost reduction with automation. Big Data is also divided into external and internal data. this study shows that the sharing of external data does not affect the incumbent firm's algorithms for consumer marketing while lessening the entry firm's entry barrier. Improving the incumbent firm's algorithms for cost reduction (with and without automation) and external data can be an entry barrier for the entry firm. These findings can be helpful (1) to analyze the effect of AI, Big Data, and data sharing on market structure, market competition, and firm behaviors and (2) to design policy for AI and Big Data.

• Journal of Korea Game Society
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• v.21 no.6
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• pp.87-96
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• 2021

As the industry develops, the technology used for games is also being advanced. In particular, AI technology is used to game automation and intelligence. These game player patterns are widely used in online games such as player matchmaking, generation of friendly or hostile NPCs, and balancing of game worlds. This study proposes a model generation method for game players. For model generation, attributes such as hunting, collection, movement, combat, crisis management, production, and interaction were defined, and patterns were extracted and modeled using decision tree method. To evaluate the proposed method, we used the game log of a commercial game and confirmed the meaningful results.

• 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.

• Journal of Digital Contents Society
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• v.12 no.2
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• pp.217-223
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• 2011

AI(Artificial Intelligence) refers to the area of computer engineering and IT technology that focuses on the methodology and creation of intelligent agents. The Othello game is often produced with AI, since it is played with relatively simple rules on a board and on a limited space of 8 rows and 8 columns. Previous algorithms take longer time than desirable and often fail to face new circumstances, as they search for all the possible cases and rules. In order to solve this crucial weakness, we propose that a CBR algorithm be applied to Orthello. Case-Based Reasoning(CBR), is the process of solving new problems based on the solutions of the past similar problems. We can apply this process to Othello and expedite the process of computer reasoning for a solution to new cases based on the data from accumulated past cases. Then, these new solutions are dynamically added to the set of past cases so that it becomes harder for players(users) to be able to read the pattern. The proposed system in which a CBR algorithm is applied to the Othello game makes the computation process faster and the game harder to play.

• Journal of the HCI Society of Korea
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• v.13 no.3
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• pp.65-73
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• 2018

Game balance settings are crucial to game design. Game balancing must take into account a large amount of numerical values, configuration data, and the relationship between elements. Once released and served, a game - even for a balanced game - often requires calibration according to the game player's preference. To achieve sustainability, game balance needs adjustment while allowing for small changes. In fact, from the producers' standpoint, game balance issue is a critical success factor in game production. Therefore, they often invest much time and capital in game design. However, if such a costly game cannot provide players with an appropriate level of difficulty, the game is more likely to fail. On the contrary, if the game successfully identifies the game players' propensity and performs self-balancing to provide appropriate difficulty levels, this will significantly reduce the likelihood of game failure, while at the same time increasing the lifecycle of the game. Accordingly, if a novel technology for game balancing is developed using artificial intelligence (AI) that offers personalized, intelligent, and customized service to individual game players, it would bring significant changes to the game production system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.349-350
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• 2019

게임에서 장르를 불문하고 모든 몬스터와 NPC는 AI를 가지고 있다. 따라서 적 몬스터 캐릭터와 전투를 즐기는 액션 게임의 경우 그만큼 인공지능이 게임 안에서 차지하는 비율이 높다고 할 수 있을 것이다. 본 논문에서는 FSM, HFSM, BT와 같은 AI 기법을 비교하여 분석하였다. 각 기법에는 주의해야 할 점이 명확하게 존재하기 때문에 구체적으로 어떠한 문제점들이 존재하는지에 대한 결과를 얻는데 연구 목적이 있다. 따라서 몬스터 AI를 구현할 때 각 인공지능 기법의 장단점을 고려하여 설계하여 유지 보수를 줄이는 방법을 연구해야 한다는 것을 확인할 수 있었다.