• Journal of Digital Convergence
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• v.12 no.1
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• pp.555-561
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• 2014

Artificial intelligence in the game is mainly used to determine patterns of behavior of NPC (Non Player Character) and the enemy, path finding. These artificial intelligence is implemented by FSM (Finite State Machine) or Flocking method. The number of NPC behavior in FSM method is limited by the number of FSM states. If the number of states is too small, then NPC player can know the behavior patterns easily. On the other hand, too many implementation cases make it complicated. The NPC behaviors in Flocking method are determined by the leader's decision. Therefore, players can know easily direction of movement patterns or attack pattern of NPCs. To overcome these problem, this paper proposes agonistic behaviors(attacks, threats, showing courtesy, avoidance, submission)in animals to apply for the NPC, and implements agonistic behaviors using Unity3D engine. This paper can help developing a real sense of the NPC artificial intelligence.

• 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.

• Journal of Korea Game Society
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• v.19 no.2
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• pp.23-32
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• 2019

The popularization of VR content is slow. It's because they have not created a new visual experience, that is, 'utility' beyond 'interest'. The utility of VR content starts from functional reality. And to enhanced it, realistic interaction is required. Specifically, this study presents three methods of network play, character artificial intelligence, and Haptic implementation. In order to confirm the hypothesis, we conducted all phases of VR content production from baseball to contents production, play test, and technical verification. Through the test of the user and the evaluation institution about the final product, it was evaluated that it contributed to the realization of the content realism through the realistic visual effect, the play presentation, and the impact evaluation by the vibration.

• Journal of Korea Game Society
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• v.8 no.4
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• pp.95-105
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• 2008

In virtual games, Non-Playing Character(NPC)s as game elements tend to frequently communicate with game players. Although the artificial-intelligence (AI) algorithm widely used for games has been greatly developed, basic roles of NPCs have remained on the same. In a life game whose goal is to observe the actions and behaviors of the human-like NPCs, for example, their straightahead actions cause boredom. Actually, NPCs fail to display their various expressions that are characterized by humans. To make NPCs act like humans, several characters with a greater variety of characteristics need to be created. this paper proposes how NPCs both express the wide range of emotions using probability distribution and react based on their different characteristics. To verify the change of NPC actions, personalities were assigned according to the probability distribution and this algorithm was applied to a 3D game to validate the method suggested in this paper.

• Journal of Korea Game Society
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• v.9 no.3
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• pp.139-150
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• 2009

This paper attempts to investigate the establishment of an interactive knowledge base for action planning by virtual agents and an interactive knowledge-based planning system. A fixed knowledge base is unable to properly handle a change in circumstances because fixed planning is only available under a fixed knowledge base. Therefore, this paper proposes the establishment of an interactive knowledge base which is applicable to diverse environments and an artificial intelligence planning system in which an interactive knowledge base is available. The interactive knowledge base proposed in this paper consists of motivation, behavior, object and action. The association relationship between knowledge base and its input is set using an automation tool. With this tool, a user can easily add to or amend the components of the knowledge base. With this knowledge base, a character plans all action items and chooses one of them to take an action. Since a new action can be applicable by updating the knowledge base even when the character environment changes, it is very useful for virtual reality content developers. This paper has established a relationship between scalable interactive knowledge base components and other components and proposes a convenient input tool and a planning system algorithm effective for an interactive knowledge base. The results of this study have been verified through testing in a virtual environment ('virtual library').

• Smart Media Journal
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• v.5 no.2
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• pp.33-41
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• 2016

FPS Games are usually centered on a combat game play where the player plays through a first-person perspective as the in-game character, in order to strike the opponent in accordance with each level's objective. In such type of game play, the decision making that leads the player to take certain actions is carried out based on the player's visual cognitive information, and information collected both directly/indirectly via previous game play experiences. Particularly in the case of a FPS game where the mutual interaction between the player and each game level is the key, an analysis of a FPS game player's visual cognitive information can provide intelligence which can help design or adjust structures of a game level. For this thesis, a sample group has been collected and divided into a novice group and an expert group based on their level of experience with FPS games. Then, using eye-tracking equipments, the point of gaze of players in each group were recorded whilst they were playing levels of a well-known FPS title. The point of gaze in the moment the player starts to take actions -right before/after the start of a combat- was recorded in 500 play videos, and as a result each group's intrinsic pattern of gaze could be identified. Through these results, the author plans to develop a methodology that can enhance the difficulty setting and the playability of FPS game levels.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.469-472
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• 2005

이 논문은 게임물리엔진에서 게임세계의 물리적인 요소를 통하여 게임에 존재하는 캐릭터들에게 인공지능을 부여하기 위한 연구에 관해서 다룬다. 게임속에서의 물리적인 상황을 자동인식하기 위해서 신경망을 이용하였다. 게임속에서의 인공지능의 적용은 게임의 속도저하를 가져오게 되는데 이 논문에서는 그러한 단점을 보완하기 위하여 물리엔진에서 캐릭터의 움직임을 계산하는 수치적분 메서드들에 대한 각 물리상황에 따른 최적의 성능을 분석하여 각각의 물리 상황마다 다른 수치 적분 메서드를 적용하는 내부 구조를 취하였다. 수치적분 메서드에 대한 각각의 성능 분석은 세가지의 물리적 상황을 구분하여 그에 기반하여 실험되었다. 인공지능 캐릭터에 대한 실험은 신경망의 토폴로지에 대한 변화와 학습 횟수에 대한 변화 및 은닉층에 대한 변화로 신경망에서의 최적의 성능에 대한 평가를 실시하였다.

• Journal of Korea Multimedia Society
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• v.12 no.5
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• pp.745-756
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• 2009

An autonomous virtual character can conduct itself like a human after recognizing and interpreting the virtual environment. Artificial vision is mainly used in the recognition of the environment for a virtual character. The present artificial vision that has been developed takes all the information at once from everything that comes into view. However, this can reduce the efficiency and reality of the system by saving too much information at once, and it also causes problems because the speed slows down in the dynamic environment of the game. Therefore, to construct a vision system similar to that of humans, a visual observation system which saves only the required information is needed. For that reason, this research focuses on the descriptive artificial intelligence engine which detects the most important information visually recognized by the character in the virtual world and saves it into the memory by degrees. In addition, a visual system is constructed in accordance with an image transaction theory to make it sense and recognize human feelings. This system finds the attention area of moving objects quickly and effectively through the experiment of the virtual environment with three dynamic dimensions. Also the experiment enhanced processing speed more than 1.6 times.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.153-167
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• 2005

In this paper, we consider the problem of exploring unknown environments with a mobile robot or an autonomous character agent. Traditionally, research efforts to address the space exploration problem havefocused on the graph-based space representations and the graph search algorithms. Recently EXPLORE, one of the most efficient search algorithms, has been discovered. It traverses at most min$min(mn, d^2+m)$ edges where d is the deficiency of a edges and n is the number of edges and n is the number of vertices. In this paper, we propose DFS-RTA* and DFS-PHA*, two real-time graph search algorithms for directing an autonomous agent to explore in an unknown space. These algorithms are all built upon the simple depth-first search (DFS) like EXPLORE. However, they adopt different real-time shortest path-finding methods for fast backtracking to the latest node, RTA* and PHA*, respectively. Through some experiments using Unreal Tournament, a 3D online game environment, and KGBot, an intelligent character agent, we analyze completeness and efficiency of two algorithms.

• 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.