• Journal of Korea Game Society
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• v.16 no.5
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• pp.79-88
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• 2016

This study analyzes the status of female characters in several fighting action games. From July 15 to 17, 2016, the world-famous fighting action game tournament EVO 2016 was held in Las Vegas, Nevada. In this tournament, nine fighting action games were selected for competition, and this study examines the status of female characters on the basis of 1) Appearance 2) Ability in these fighting actions games. After doing comparison between female characters and male characters in the fighting action game, it reflects on the status and right of female characters in the virtual world.

• Digital Contents
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• no.5 s.132
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• pp.110-112
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• 2004

갯앰프드는 흔히 일컫어지는 캐주얼 게임으로 2.5등신의 귀여운 3D캐릭터들이 허둥지둥 액션을 펼치는 네트워크 대전게임이다. 얼핏보면 그저 머리 큰 캐릭터들이 우왕좌왕 좌충우돌로 뛰어 다니며 서로 치고 박는 단순한 액션게임으로 볼수 있으나 게임 내에 들어 있는 코믹한 요소들과 게임 안의 게임들이 얽혀 있어 다양한 재미를 제공한다. 최대 8명까지 참가 가능한 이 게임은 쉬운 조작법으로 누구나 쉽게 즐길 수 있고 격투가, 스파이, 초인 등 8가지 캐릭터 가운데 하나를 선택해 유저개성에 맞게 캐릭터의 얼굴, 헤어스타일, 체형 등을 자유롭게 만들 수도 있다. 유저가 만들 캐릭터로 겟앰프드 대전에 참가하는 것 또한 다른 게임에서는 볼수 없었던 재미로 게임 중 깃발 뺏기, 농구, 축구, 풍선터뜨리기 게임등 토너먼트 게임도 즐길수 있어며, 다양한 무기 아이템등을 활용해 대전하면 게임을 더욱 박진감 있게 즐길 수 있다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.39-46
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• 2020

Recently, for designing virtual characters in the battle game field effectively, some methods are very needed to predicate the win rates of the battle of them efficiently. In this paper, we propose a method to solve this problem by combining simulation and machine learning. Firstly, a simulation is used to analyze the win rates of the battle of virtual characters in the battle game. In addition, we apply a regression model based machine learning scheme to predict win rates of the battle of virtual characters according to their abilities. Our experimental results using suggested method show that it is almost no difference between the win rates of the simulation and the prediction results using the machine learning scheme. And also, we can obtain good performance in the experiment using only simple regression based machine learning model.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.329-336
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• 2005

This paper proposes a method to provide intelligence for characters in fighting action games by using genetic algorithm. The proposed characters without any knowledge on the rules of the game learn the rules and techniques for generations, and have the capability of evolving. To evaluate adaptability for varying circumstances, we changed the rules and compared the results. The experimental results show that the intelligent characters can adapt to the new rules. An advantage of the proposed method is that it can be easily applied to characters for other category of games such as PC games and internet online games.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.831-840
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• 2004

In this paper, we propose a scheme to implement intelligent game characters based on neural networks. Neural networks that implement in-telligent game character receive the action of an opponent character and the distance between them, decide intelligent character's action, and output the decision. The neural networks are trained by reinforcement learning using the scores acquired by the actions of two characters as reinforcement values. To show the usefulness of the proposed scheme, a simple fighting action game is implemented and various experiments are performed. Experimental results show that proposed intelligent characters can learn the rule of the game. The proposed scheme can be ap-plied to massively multiple online games as well as fighting action games.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.449-456
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• 2006

This paper proposes intelligent characters for fighting action games to which energy points are applied for more realistic implementation than those of previous researches. The intelligent characters decide their actions in consideration of their energy level as well as a current action, the step of the action, the distance, and past actions of opponent characters that were used in existing intelligent ones. We used two types of energy, HP(Health Point) and MP(Mana Point) that were frequently employed in recent on-line games. We experimented with proposed intelligent characters to investigate whether the intelligent characters learn proper actions and cope with opponent characters in consideration of their energy levels. Experimental results showed that the intelligent characters reacted with the best actions to obtain high score if their energy is sufficient, Otherwise, they did the actions to(that?) recharge their energy. From this observation, we could conclude that the proposed intelligent characters worked well and did effective actions in consideration of the their energy.

• Journal of Korea Game Society
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• v.7 no.3
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• pp.23-30
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• 2007

Game balance is an very important factor for game design, it is necessary factor to make game more fun. Most of game companies have been using their own balance method for successful game development. An efficient balance method always can't guarantee successful game development but it is true for game developer to need standard balance method for efficient game development. Fighting system between characters is mainly implemented in genre including action. Direct balance factors for fighting between characters are their characteristics and attack patterns. In this paper, We identify direct balance factors for game including fighting system between characters and propose mathematical method for it.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.6
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• pp.69-80
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• 2004

This paper proposes a method to learn action patterns of opponent characters for intelligent characters. For learning action patterns, intelligent characters learn the past actions as well as the current actions of opponent characters. Therefore, intelligent characters react more properly than ones without the knowledge on action patterns. In addition, this paper proposes a method to learn moving actions whose fitness is hard to evaluate. To evaluate the performance of the proposed algorithm, we experiment with four repeated action patterns in a game similar to real games. The results show that intelligent characters learn the optimal actions for action patterns and react properly against to random action opponent characters. The proposed method can be applied to various games in which characters confront each other, e.g. massively multiple of line games.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.117-120
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• 2007

최근 전 세계적으로 모바일장치의 확산과 초고속 인터넷의 확장에 따라 모바일게임시장이 더욱 확대되고 있다. 모바일게임은 기술의 이제 사회적으로 분명한 대중문화로서 자리 잡고 있다[1]. 그러나 기존의 모바일 게임들은 오프라인 형태로 사용자가 한 번 다운 받아 즐기는 게임이거나 온라인 네트워크 모바일 게임의 형태이라 할지라도 배틀 형태의 대전 게임으로만 이루어져 있는 종류의 게임이 대부분이다. 본 논문은 온라인 배틀 게임의 형태와 캐릭터를 육성 시키는 게임의 형태를 혼합하여 다양한 방법으로 하나의 게임을 즐길 수 있도록 한 줄다리기 게임을 제안한다. 단순한 온라인 배틀 게임이 아닌, 오프라인의 연습과정을 트레이닝 모드라라고 정의 하여 기존의 모바일 게임들과 차별되는 혁신적인 모바일 게임 모델이다. 본 논문에서 제안하는 Rope Survival게임은 네트워크 대전게임과 캐릭터의 육성 게임을 동시에 즐기는 게임이라는 측면에서 모바일 게임의 연구 분야에 새로운 방향을 제시한다.

• Journal of Korea Game Society
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• v.17 no.3
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• pp.83-92
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• 2017

Game users can create cultural images about foreign countries from game contents what they play. Most fighting action games provide various game characters and game stages where the game characters come from. This study analyzes that Korean game characters and game stages of The King of Fighters (KOF) series, one of popular fighting action games. We find that KOF describes Korea negatively in the beginning KOF series but the negative descriptions of Korea were gradually weaken as newer KOF series were released.