• 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.

• 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.

• 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.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.83-84
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• 2019

제안 방법은 Visual Studio 2017 API 와 C++을 사용하여 2D 액션 게임은 스피드 와 3개의 스킬을 추가하여 게임 내에서 더 화려하게 싸울 수 있도록 하였으며 1 : 1 대전으로 인하여 게임의 몰 입도가 높다는게 특징이다. 제안 게임은 1 : 1 대전에 알맞은 분위기를 위해 어두운 배경 및 울퉁불퉁한 2D지형을 사용하여 C++ 의 코드를 이용하여 2D 애니매이션을 사용한다.

• Journal of Korea Game Society
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• v.8 no.1
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• pp.15-27
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• 2008

Games can be divided into different genres such as action, adventure, role playing, simulation and etc based on the types of the play provided to a player. And each genre has various kinds of balancing system. Fighting games are classified as one of the action genres. They seem to have a simple structure but there is a variety of complex balancing factors. Because there are quite a number of the fighting games that were developed in a relatively short period, it is possible to identify diverse balancing systems in the fighting games from unstable ones in the early stage to the recent ones in a stable condition. The balancing system allows players to use all the contents offered in the game by eliminating the imbalance created among the various components. It helps players maintain their interest during the procedure. Furthermore, it gives the justification, increases the efficiency of controlling, and develops the empathy in the process. Therefore, the balancing system plays a crucial role in the games when it comes to getting an evaluation and building popularity, which can determine its lifespan. In this study, we researched on the possibilities of expanding an application to the other genres by understanding the established balancing model developed from the case study of the fighting games.

• 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.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 the Korea Computer Industry Society
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• v.7 no.3
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• pp.163-170
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• 2006

This paper proposes intelligent characters for fighting action games to which energy concepts 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 loam proper actions and cope with opponent characters in consideration of their energy levels. Experimental results showed that the intelligent characters reacted to the best actions to obtain high score if their energy is sufficient, otherwise they did the actions to recharge their energy. From this observation, we could conclude that the proposed intelligent characters worked well and did effective actions in consideration to the their energy.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.383-389
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• 2004

This paper proposes a method to provide intelligence for characters in fighting action games by using a neural network. Each action takes several time units in general fighting action games. Thus the results of a character's action are not exposed immediately but some time units later. To design a suitable neural network for such characters, it is very important to decide when the neural network is taught and which values are used to teach the neural network. The fitness of a character's action is determined according to the scores. For learning, the decision causing the score is identified, and then the neural network is taught by using the score change, the previous input and output values which were applied when the decision was fixed. To evaluate the performance of the proposed algorithm, many experiments are executed on a simple action game (but very similar to the actual fighting action games) environment. The results show that the intelligent character trained by the proposed algorithm outperforms random characters by 3.6 times at most. Thus we can conclude that the intelligent character properly reacts against the action of the opponent. The proposed method can be applied to various games in which characters confront each other, e.g. massively multiple online games.