• 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.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.818-823
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• 2006

본 연구에서는 디지털 콘텐츠와 물리적 미디어가 유기적으로 결합할 수 있는 가능성을 모색하고자, 입출력 일체화 개체인 'I/O 셀(Cell)'과 그 집합체인 '텐저블 게임 콘솔(Tangible Game Console)'을 개발하였다. I/O 셀은 여러 종류의 센서와 액츄에이터가 하나의 물리적 노드 내에 통합되어 있는 개체로, 이 I/O 셀 다수를 집적하면 다양한 크기와 용도의 엔터테인먼트 미디어를 구성하는 것이 가능하다. 그 중 하나로 개발된 텐저블 게임 콘솔은 일종의 센서-액츄에이터 집합체로 셀들 사이의 연계를 통해 다양한 물리적 정보를 인식하고 표현할 수 있다. 텐저블 게임 콘솔은 기존 게임 콘텐츠의 주요한 표현 수단이었던 이미지와 텍스트 정보 대신에 물리적 정보를 통해 게임 콘텐츠를 구성하며, 조작대상과 조작수단이 일체화되어 있고, 또한 음악, 퍼즐, 액션 등 다양한 장르의 게임 콘텐츠가 적용 가능하다는 특징을 가지고 있다. 텐저블 게임 콘솔에서는 불, 바람, 물, 생명체 등의 물리적 현상을 형상/움직임/소리/빛 등을 통해 재현하며, 사용자는 이를 때리기/바람불기/흔들기/기울이기 등의 직접적인 조작을 통해 체험할 수 있다. 또한 물리적인 상호작용 과정이 일정한 게임 규칙을 가지고 구성되어 있어 지속성 있는 놀이 경험을 제공한다. 본 연구에서는 텐저블 게임 콘솔의 콘텐츠로 음악, 액션, 대전게임 등을 개발하였으며, 이 콘텐츠들의 놀이경험을 평가함으로써, 텐저블 게임 콘솔의 발전 가능성을 확인하였다.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.435-438
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• 2005

현재 인공지능 기법은 우리 주위의 여러 분야에서 사용되고 있으며, 그 중요도가 점점 높아지고 있다. 이러한 분야 중에 하나가 바로 컴퓨터 게임 분야로 전통적인 인공지능 기법부터 근래에 게임과의 결합이 시도되고 있는 비전통적 기법에 이르기까지 다양한 종류의 방법이 시도되었고, 시도될 예정이다. 본 논문에서는 게임내에서 그 비중이 커져가고 있는 인공지능을 통한 캐릭터의 지능화 방안으로 신경망 기반의 지능 캐릭터를 제안하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.460-461
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• 2012

Currently, many PC games are developed using game engines. Although these game engines provide many functions, there are also downsides such as limitations of the engine itself and development costs. This is essentially why DirectX, which has relatively few limitations and requires minimal expenses, is still considered a very useful game development tool. This thesis lays out the design plan for a war action genre game called Aeon of Strife using low quality game development tools.

• 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.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.3 s.303
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• pp.17-28
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• 2005

Recently intelligent characters in computer games have been an important element more and more because they continually stimulate gamers' interests. As a typical method for implementing such intelligent characters, neural networks have been used for training action patterns of opponent's characters and game rules. However, some of the game rules can be abruptly changed and action properties of garners in on-line game environments are quite different according to gamers. In this paper, we address how a neural network adapts to those environmental changes. Our adaptation solution includes two components: an individual adaptation mechanism and a group adaptation mechanism. With the individual adaptation algorithm, an intelligent character steadily checks its game score, assesses the environmental change with taking into consideration of the lastly earned scores, and initiates a new learning process when a change is detected. In multi-user games, including massively multiple on-line games, intelligent characters confront diverse opponents that have various action patterns and strategies depending on the gamers controlling the opponents. The group adaptation algorithm controls the birth of intelligent characters to conserve an equilibrium state of a game world by using a genetic algorithm. To show the performance of the proposed schemes, we implement a simple fighting action game and experiment on it with changing game rules and opponent characters' action patterns. The experimental results show that the proposed algorithms are able to make intelligent characters adapt themselves to the change.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.481-482
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• 2014

본 논문에서는 동양 신화적 요소들을 게임 기획에 접목 시키는 방법을 제안하고 구현한다. 게임에 접목 시킨 동양 신화적 요소에는 사방신을 채용했으며, 각 사신이 갖는 동물적 형상과 대표 색상으로 나누어 기획에 접목시켜 게임을 디자인하였다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.83-90
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• 2024

In this paper, we propose a solution to apply a proximity voice dialog system to voice dialog technology, one of the interaction systems in multimedia environments. A voice dialog between multiple users in a multimedia space is designed by adjusting the volume of the voice according to the distance between the user avatars and muting the user who is beyond the audible distance. The main feature of this research is a reliable UDP-based active server system that delivers low-quality voice data to users who are far away based on distance and does not transmit voice data to users who enter the inaudible area for economic development. The performance of the proposed system was measured in a previously completed project based on the Unity game engine, and it is expected that the system proposed in this research will be actively used in environments that provide interaction between multiple users such as met averse content and real-time battle action games.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.42 no.3
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• pp.31-38
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• 2005

This paper proposes an implementation method of intelligent characters that can properly adapt to action patterns of opponent characters in fighting games by using genetic algorithm. For this intelligent characters, past actions patterns of opponent characters should be included in the learning process. To verify the effectiveness of the proposed method, two types of experiments are performed and their results are compared. In first experiment(exp-1), intelligent characters consider current action and its step of an opponent character. In second experiment (exp-2), on the other hands, they take past actions of an opponent characters into account additionally. As a performance index, the ratio of score obtained by an intelligent character to that of an opponent character is adopted. Experimental results shows that even if the performance index of exp-1 is better than that of exp-2 at the beginning of stages, but the performance index of exp-2 outperforms that of exp-1 as stages go on. Moreover, optimum solutions are always found in all experimental cases in exp-2. Futhermore, intelligent characters in exp-2 could learn moving actions (forward and backward) and waiting actions for getting more scores through self evolution.