• 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.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.161-168
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• 2020

Predicting outcome of the sports enables teams to establish their strategy by analyzing variables that affect overall game flow and wins and losses. Many studies have been conducted on the prediction of the outcome of sports events through statistical techniques and machine learning techniques. Predictive performance is the most important in a game prediction model. However, statistical and machine learning models show different optimal performance depending on the characteristics of the data used for learning. In this paper, we propose a new ensemble model to predict English Premier League soccer games using statistical models and the machine learning models which showed good performance in predicting the results of the soccer games and this model is possible to select a model that performs best when predicting the data even if the data are different. The proposed ensemble model predicts game results by learning the final prediction model with the game prediction results of each single model and the actual game results. Experimental results for the proposed model show higher performance than the single models.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.49-55
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• 2009

A cooperative game often arises from domination problem on graphs and the core in a cooperative game could be the optimal solution of a linear programming of a given game. In this paper, we define a {k}-fractional domination game which is a specific type of fractional domination games and find the core of a {k}-fractional domination game. Moreover, we may investigate the balancedness of a {k}-fractional domination game using a concept of a linear programming and duality. We also conjecture the concavity for {k}-fractional dominations game which is important problem to find the elements of the core.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.29
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• pp.173-196
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• 2006

Online game business has emerged as the most lucrative entertainment industry, with over 10 million players in South Korea and over 30 million in Japan in 2005. The popularity of online games can be attributed to the availability of broadband network, pushing online games into the mainstream entertainment culture. The age distribution of online game players is expanding and a variety of new games are under development to target certain age groups. While the interactive entertainment market continues to expand, with many new online game publishers entering the Japan, relatively little is known about which factors influence online game players' behavioral intentions to play continuously in this area. This study investigates major factors which influence the acceptance of online game services based on the theoretical backgrounds of the technology acceptance model(TAM) and the flow theory. This paper extended the Davis' TAM model by including the flow concept as another major factor toward the intention to play online game. Based on data collected from online questionnaire survey, we show that the proposed model provides an adequate fit to the data, and that the flow experience is another important factor influencing the intention to play online game, as well as the perceived ease of use.

• Journal of information and communication convergence engineering
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• v.16 no.3
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• pp.189-196
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• 2018

Player's actions in a game occur in the process of gameplay experiences in a play space designed by the developer according to preset gameplay beats. Focusing on beats that induce a first-person shooter (FPS) game's main gameplay, this paper analyzes the differences in eye gaze information found in different players during the course of gameplay. For this research goal, the study divides the beat areas in which play actions appear in association with gameplay beats at a typical FPS game level, repeatedly conducts tests in accordance with a player's experience level (novice and expert group), and collects and analyzes eye gaze information data in three types of beat areas. The analysis result suggests concrete guidelines for game level design for different beat areas based on an FPS game player's experience level. This empirical experiment method and result can lessen repetitive modification work for game level design and consequently be utilized for optimizing the game level to the developer's intention.

• Korean Journal of Child Studies
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• v.26 no.1
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• pp.31-46
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• 2005

This study examined internet game addiction and college adaptation(academic, social, personal-emotional, institutional attachment) by Internet game motivation types. The 475 subjects (235 male and 240 female college students) responded to the newly developed self-report measure, Internet Game Motivation Scale (IGMS). Cluster analyses of IGMS data identified 5 types of Internet game motivation: Active I, Active II, Moderate I, Moderate II, Passive. Active (I, II) types had the highest scores in Internet game addiction while the Passive type yielded the lowest. Except for academic adaptation, all aspects of college adaptation varied as a function of Internet game motivation types. Overall, Moderate II showed highest whereas Active I showed lowest academic adaptation scores.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.12
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• pp.1657-1664
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• 2018

This paper proposes a decision making scheme for choosing the best move at each state of game in order to implement an artificial intelligence othello game player. The proposed decision making scheme predicts the various possible states of the game when the game has progressed from the current state, evaluates the degree of possibility of winning or losing the game at the states, and searches the best move based on the evaluation. In this paper, we generate learning data by decomposing the records of professional players' real game into states, matching and accumulating winning points to the states, and using the Artificial Neural Network that learned them, we evaluated the value of each predicted state and applied the Minimax search to determine the best move. We implemented an artificial intelligence player of the Othello game by applying the proposed scheme and evaluated the performance of the game player through games with three different artificial intelligence players.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1077-1084
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• 2015

Security issues such as an illegal acquisition of personal information and identity theft happen due to using game bots in online games. Game bots collect items and money unfairly, so in-game contents are rapidly depleted, and honest users feel deprived. It causes a downturn in the game market. In this paper, we defined the growth types by analyzing the growth processes of users with actual game data. We proposed the framework that classify hard-core users and game bots in the growth patterns. We applied the framework in the actual data. As a result, we classified five growth types and detected game bots from hard-core users with 93% precision. Earlier studies show that hard-core users are also detected as a bot. We clearly separated game bots and hard-core users before full growth.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.322-331
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• 2003

As the complexity of a 3D game is increased by various factors of the game scenario, it has a problem for controlling the interrelation of the game objects. Therefore, a game system has a necessity of the coordination of the responses of the game objects. Also, it is necessary to control the behaviors of animations of the game objects in terms of the game scenario. To produce realistic game simulations, a system has to include a structure for designing the interactions among the game objects. This paper presents a method that designs the dynamic control mechanism for the interaction of the game objects in the game scenario. For the method, we suggest a game agent system as a framework that is based on intelligent agents who can make decisions using specific rules. Game agent systems are used in order to manage environment data, to simulate the game objects, to control interactions among game objects, and to support visual authoring interface that ran define a various interrelations of the game objects. These techniques can process the autonomy level of the game objects and the associated collision avoidance method, etc. Also, it is possible to make the coherent decision-making ability of the game objects about a change of the scene. In this paper, the rule-based behavior control was designed to guide the simulation of the game objects. The rules are pre-defined by the user using visual interface for designing their interaction. The Agent State Decision Network, which is composed of the visual elements, is able to pass the information and infers the current state of the game objects. All of such methods can monitor and check a variation of motion state between game objects in real time. Finally, we present a validation of the control method together with a simple case-study example. In this paper, we design and implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the most effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.11
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• pp.2563-2568
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• 2012

The growth speed of the industry of national smart phone game service has become faster and the its economical and cultural effect has increased steadily these days. The DB design for data perpetuation in C/S MMO which has accumulated data a lot and many simultaneous plays like smart phone games is very important. This paper, mindful of the operation and the scalability suitable for android OS, by utilizing RDBMS, enabling very short, many transactions, their complexity, and a large amount of game data process, suggests fully-qualified MMORPG game service architecture design techniques widely available for more than online game industries.