• Journal of Internet Computing and Services
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• v.11 no.3
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• pp.25-32
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• 2010

It takes quite amount of time to study a game because there are many game characters and different stages are exist for games. This paper used reinforcement learning algorithm for characters to learn, and so they can move intelligently. On learning early, the learning speed becomes slow. Improved sequential prediction method was used to improve the speed of learning. To compare a normal learning to an improved one, a game was created. As a result, improved character‘s ability was improved 30% on learning speed.

• Journal of Korea Game Society
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• v.20 no.1
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• pp.83-92
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• 2020

The WHO has defined gaming disorder as a disorder, and there are arguments for and against it from different perspectives. In response, this paper tried to identify the disease model, psychosocial, and cultural perspective for complementing them with an interdisciplinary attitude. First, universal prevention should be provided for general game users to get literacy. Second, selective prevention should be provided for a potentially risky group to find out the alternative activity. Finally, indicated prevention should be provided for a risky group to be treated.

• Electronics and Telecommunications Trends
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• v.32 no.4
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• pp.96-103
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• 2017

최근 모바일 게임 산업에서는 게임 이용 중 게임 내 필요한 아이템을 별도로 구매하는 부분 유료화 비즈니스 모델이 지속해서 성장하고 있다. 부분 유료화 게임은 이용자 측면에서 접근성이 용이하며, 게임 제공자 측면에서는 구매를 유도하는 방법들이 고도화되었다. 인기게임의 경우 부분 유료화로 인한 수입이 지속적으로 증가하고 있다. 본고에서는 모바일 부분 유료화 게임에 대해 게임 운영을 최적화하기 위한 기술들을 살펴보고자 한다. 먼저, 게임 현황 파악 및 분석을 위한 대표적인 게임운영지표 분석 솔루션들을 요약하고, 게임운영지표를 개선하기 위한 게임 이용자 행동예측 기술들을 소개한다. 또한, 최근 연구되고 있는 모바일 게임 분석 기술의 한계점을 돌아보고 향후 연구 방향에 관해 기술한다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.164-168
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• 2006

최근 컴퓨터 게임에 등장하는 NPC(Non-player Character)에 각종 인공지능 기법을 적용하는 연구들이 이루어지고 있다. 하지만 대부분의 연구가 플레이어를 상대하는 적대적 입장의NPC들의 움직임 조절에 초점을 맞추고 있고 아직까지 게임 상에서 등장하는 모든 NPC는 항상 같은 말과 비슷한 행동을 되풀이하는 모습만을 보여주고 있다. 이는 플레이어가 게임을 비현실적으로 느끼게 만들고 결과적으로 게임의 재미를 저하시키는 요소로 작용한다. 플레이어에게 보다 현실적인 게임 환경을 제공하기 위해서는NPC가 단순히 게임의 배경을 구성하는 오브젝트가 아니라 다양한 대화를 통해 플레이어에게 많은 영향을 주게 하여 게임의 기여도를 높여주어야 한다. 본 논문에서는 이를 위해 게임 속에서 주어지는 퀘스트를 구성하는 NPC, 몬스터, 보상 등의 속성 정보를 온톨로지화 하고, 인터넷에 존재하는 게임 커뮤니티에서 퀘스트 질의/응답 게시판의 글을 추출하여, 플레이어의 관련 질의에 응답하는 NPC를 구현하고자 한다. 이를 위해 온톨로지 정보를 이용한 검색 알고리즘을 구현하였고,시뮬레이션을 통해 NPC가 커뮤니티 게시글 정보를 이용하여 유저에게 고정되지 않은 다양한 메시지를 전달하면서 동시에 유저의 게임 진행을 도와주는 모습을 확인하였다.

• 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.

• Smart Media Journal
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• v.13 no.2
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• pp.32-43
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• 2024

Game agents which are behavioral agent for game playing are a crucial component of game satisfaction. However it takes a lot of time and effort to create game agents for various game levels, environments, and players. In addition, when the game environment changes such as adding contents or updating characters, new game agents need to be developed and the development difficulty gradually increases. And it is important to have a game agent that can be customized for different levels of players. This is because a game agent that can play games of various levels is more useful and can increase the satisfaction of more players than a high-level game agent. In this paper, we propose a method for learning and controlling the level of play of game agents that can be rapidly developed and fine-tuned for various game environments and changes. At this time, reinforcement learning applies a policy-based distributed reinforcement learning method IMPALA for flexible processing and fast learning of various behavioral structures. Once reinforcement learning is complete, we choose actions by sampling based on Softmax-Temperature method. From this result, we show that the game agent's play level decreases as the Temperature value increases. This shows that it is possible to easily control the play level.

• Journal of Korea Game Society
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• v.12 no.6
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• pp.33-46
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• 2012

This research aimed to analyze the way of how flying movement system and background graphic in MMORPG, which provides movement system, influences on users' psychological behavior in order to enhance user's participation and the completion level of 3D game. This research also analyzed the difference between the user's response to the flying movement and other movement structure in game such as things to board, summons, portal, chase, and etc. With this comparative analysis, this study could clarify the characteristics of the user's desire to fly according to the virtual world' topography in game, the necessity of flying movement for fast movement, the dependence of user on flying system, the immersion according to behavior in process of flying, and expression level of virtual world, and etc. The survey through the case research of MMORPG Aion selected for this research showed flying movement system can have suitable quality by applying users' playing time and absorption in flight process, users' impulse following game level, and users' need to fly as a convenience of flying properly.

• Journal of the Korea Convergence Society
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• v.11 no.1
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• pp.131-139
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• 2020

Gamers switch their games to a new when get bored or encounter more attractive ones. Switching cost varies by gamers and depends on how they are satisfied with their current game. This study evaluates the satisfaction with current games as the miltiple basic psychological need in the self-determination theory and suggests 'needs-costs' causality research model that explain the variety of gamer's switching behavior. As the empirical test to domestic mobile gamers, the autonomy fulfillment to current game affect reversely with those of autonomy and relatedness. Those relationships between need satisfaction and perceptual switching cost vary according to their age and game genre preference. The results would be applied to understand gamers' switching behavior.

• Journal of Korea Game Society
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• v.2 no.2
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• pp.60-65
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• 2002

In the majority of today's computer games, the behaviour of characters are controlled by pre-defined game logic or pre-generated motion. As game developers strive for richer and more interactive games, they often encounter limitations with this approach. This paper attempts to construct a game model using Genetic Algorithms (GAs) in order to produce more intelligent and compelling computer games. Based on teaming ability, the use of GAs will enable the characters to continually evolve, providing a changing and dynamic game environment. A real-time game was implemented to investigate the performance and limitations of the system.

• 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.