• Journal of Korea Multimedia Society
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• v.7 no.6
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• pp.814-823
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• 2004

Current video games and game room games are played as a single player mode on the basis of various emulators. With the evolution of data communications and game technology, a new trend in the game industry has made the primary interests of game developers and companies in the game industry be moved toward a multiplayer mode from the traditional single player mode. In this paper, we represent how to implement a network game platform by allowing network modules to be run in conjunction with the current video emulator games. It also suggests a synchronization scheme for real-time game playout and practical mechanism that can support network games to be played with the Peer-to-Peer process using a lobby system.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.7
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• pp.167-176
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• 2019

Recently, as the types of services that can be enjoyed in mobile telecommunication networks such as social networks and video streaming are increasing, mobile users(MUs) can access mobile contents easily by consuming mobile data. However, under a mobile telecommunication environment, MUs have to pay a high data fee to a network service provider(SP) in order to enjoy contents. The 'data sponsor' technique, introduced as a way to solve this problem, has attracted attention as a breakthrough method for enhancing contents accessibility of MUs. In this paper, we propose an algorithm that determines the optimal discount rate through the Stackelberg game in the data sponsor environment. We also propose an algorithm to design edge caching, which caches highly popular content for MUs on edge server, through many-to-many matching game. Simulation results clearly indicate that the profit for CP's content consumption is improved by about 6~11%, and the profit of CP according to the ratio of edge caching is improved by about 12% than the other existing schemes under data sponsor environment.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.1
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• pp.55-64
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• 2022

This study analyzes the success factors of OTT original content through big data, and intends to suggest scenarios, casting, fun, and moving elements when producing the next work. In addition, I would like to offer suggestions for the success of 'Squid Game Season 2'. The success factor of 'Squid Game' through big data is first, it is a simple psychological experimental game. Second, it is a retro strategy. Third, modern visual beauty and color. Fourth, it is simple aesthetics. Fifth, it is the platform of OTT Netflix. Sixth, Netflix's video recommendation algorithm. Seventh, it induced Binge-Watch. Lastly, it can be said that the consensus was high as it was related to the time to think about 'death' and 'money' in a pandemic situation. The suggestions for 'Squid Game Season 2' are as follows. First, it is a fusion of famous traditional games of each country. Second, it is an AI-based planned MD product production and sales strategy. Third, it is casting based on artificial intelligence big data. Fourth, secondary copyright and copyright sales strategy. The limitations of this study were analyzed only through external data. Data inside the Netflix platform was not utilized. In this study, if AI big data is used not only in the OTT field but also in entertainment and film companies, it will be possible to discover better business models and generate stable profits.

• Journal of Korea Game Society
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• v.16 no.3
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• pp.35-44
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• 2016

The progress of the game engine make us easier to develop a game product and it is possible for one man to make a game. This means that if you have ideas about game, you can make a game as you thought only if you know how to design a game. Because the game designing is not the field that may not be used like tools and could not buy like graphical data. This study, based on the practice, presented to the understanding and guidance for people who want to know about game designing such as no experienced person and starting new game design. These contributions are supported by a detailed exploration of common understandings of game design methods so that I expect to help designing or managing the project.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1161-1170
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• 2021

The MOBA game is updated every year with a new season, the new season update brings fun to the game users and also makes them feel unfamiliar with the new gameplay. In order to let game users better adapt to the new season, this study will extract the new season balance evaluation factors. Firstly, we used one-on-one review to collect the unbalanced factors that game users had encountered at the beginning of the new season of the MOBA game, and secondly, we organized the collected review data into a questionnaire and conducted a survey. The first step of the experiment was to filter out the lower factors through exploratory factor analysis and extract the balance evaluation factors of the MOBA game new season. The second step of the experiment was to examine the correlation between the factors through confirmatory factor analysis, as well as to confirm the appropriateness and explanatory value of the factors. The analysis resulted in "Game character experience", "Game view's expression", "Game level", and "Composition of the game setting" are the four factors. Finally, through correlation analysis, the most relevant factor for game satisfaction is the "Game character experience", and each factor is correlated with each other.

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.37-44
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• 2017

Games of the AOS genre are classified as an e-sport rather than a recreational computer game. The involved statistical analyses such as game playing patterns and the season's characters gain importance due to the expertise-requiring nature of sports. In this study, the strategic analysis of computer games was conducted by using data mining techniques on League of Legend, a representative AOS game. We designed and tested a winning forecast model using winning percentage prediction techniques such as logistic regression analysis, discriminant analysis, and artificial neural networks. The game data analysis results were represented by a probabilistic graph and used in the visualization tool for game play. Experimental results of the winning forecast model showed a high classification rate of 95% on average with potential for use in establishing various strategies for game play with the visualization tool.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.137-148
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• 2017

Researches on the Artificial Intelligence has been explosively activated in various fields since the advent of AlphaGo. Particularly, researchers on the application of multi-layer neural network such as deep learning, and various machine learning algorithms are being focused actively. In this paper, we described a development of an artificial intelligence Janggi game based on reinforcement learning algorithm and MCTS (Monte Carlo Tree Search) algorithm with accumulated game data. The previous artificial intelligence games are mostly developed based on mini-max algorithm, which depends only on the results of the tree search algorithms. They cannot use of the real data from the games experts, nor cannot enhance the performance by learning. In this paper, we suggest our approach to overcome those limitations as follows. First, we collects Janggi expert's game data, which can reflect abundant real game results. Second, we create a graph structure by using the game data, which can remove redundant movement. And third, we apply the reinforcement learning algorithm and MCTS algorithm to select the best next move. In addition, the learned graph is stored by object serialization method to provide continuity of the game. The experiment of this study is done with two different types as follows. First, our system is confronted with other AI based system that is currently being served on the internet. Second, our system confronted with some Janggi experts who have winning records of more than 50%. Experimental results show that the rate of our system is significantly higher.

• Journal of Korea Game Society
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• v.18 no.6
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• pp.69-82
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• 2018

The need of high quality VR contents has been steadily raised in recent years. Therefore, this study investigated the user's interest factors of VR game which is receiving the most attention among VR contents. We used STEAM review data and applied Text mining and Network analysis to perform this research. As a result, it was possible to confirm 4 word clusters related VR game users. Each cluster is named by 'presence', 'first person view game', 'auditory factor' and 'interaction'. This study has its meaning. First, user related research would be very helpful to develop high quality VR game. Second, it confirms that review data of VR game users can be structured, analyzed and used.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1097-1104
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• 2021

Online game-bots are already known for a lot of persons by various ways. It leads to problems such as declining game player's interest, in-game financial crisis, etc. Detecting and restricting of game-bot is now essential. Because both publishers and players get disadvantages from their long term abnormal working. But it is not easy to restrict, because of false restriction risks. Game publishers need to distinguish game-bot from server-side game logs. At last, it should can make reasons for game-bot restriction. In this paper, we classified game-bot users by using daily separated game logs for testing data. For daily-driven detection, we separated total dataset into one day logs. Preliminary detects game-bots with one day logs, and determines total results by using these data. Daily driven detection advantages on detection which contains combined game playing style. Which shows like normal user and game-bot. These methodology shows better F1-score, which one of indicator which demonstrate classification accuracy. It increases from 0.898 to 0.945 by using Random Forest classifier.

• Journal of Korea Game Society
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• v.9 no.2
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• pp.125-133
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• 2009

Game artificial intelligence is usually used to provide intelligent and adjusted game environment for user. Previously, it was used for Non-player character(NPC) playing a role of a company or an enemy through collecting and analyzing a user's behaviour. However, it was just mimicking the user's behavior. This paper introduces a method to change game environment by analyzing a user's game behavior. Game behavior data has been used to understand user's game preference. Also, the user's preference was used to provide more active game environment by reflecting decision of geographical features, items and distribution of NPC. For experiment of the suggested method, we utilized a real 2D action game and confirmed the game environment which changing properly according to the user's game play.