• Journal of Korea Game Society
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• v.18 no.3
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• pp.87-102
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• 2018

In this study of effective game localization, we attempt to gauge the influence of characteristics on the translation of the texts in games. In general, the characters in the games that feature interactive story-telling structure have a huge impact on events that occur in the games. Additionally, in case the origin of the characters are closely connected with either cultural factors or symbolisms, the relation between characters and stories tends to be stronger. In this research, the characteristics of the characters in the game 'Lobotomy Corporation' - featuring characteristics based on 'The Tree of Sepiroth' of Kabbalah - will be analyzed in depth; then, the result will lead us to suggest the method of proper translation in order to show how to localize the games effectively in future.

• Journal of Game and Entertainment
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• v.2 no.1
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• pp.8-16
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• 2006

The development of multicast communication services in the Internet is expected to lead a stable packet transfer even though On-Line Games generate heavy traffic. The Core Based Tree scheme among many multicast protocols is the most popular and suggested recently. However, CBT exhibits two major deficiencies traffic concentration or poor core placement problem. Thus, measuring the bottleneck link bandwidth along a path is important to understand the performance of multicast. We propose a method in which the core router's state is classified into SS(Steady State), NS(Normal State) and BS(Bottleneck State) according to the estimated link speed rate, and also the changeover of multicast routing scheme for traffic overload. In addition, we introduce Anycast routing tree, an efficient architecture for constructing shard multicast trees.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1275-1281
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• 2011

Many authors studied cooperative games that arise from variants of dominating set games on graphs. In wireless networks, the connected dominating set is used to reduce routing table size and communication cost. In this paper, we introduce a connected dominating set game to model the cost allocation problem arising from a connected dominating set on a given graph and study its core. In addition, we give a polynomial time algorithm for determining the balancedness of the game on a tree, for finding a element of the core.

• Journal of the military operations research society of Korea
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• v.36 no.3
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• pp.83-93
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• 2010

In this paper, we propose a new expert system for close combat in military war game model for training. Simulation logic for damage assesment is one of the main simulation functions in military war game. In Changcho 21's model which is the war game model for Republic of Korea Army corps and division, the main function of close combat's damage assessment has not been calculated by Changcho 21's model, but by COBRA which was made by US Army and has been the expert system for close combat. Results which were calculated in COBRA were sent to Changcho 21's model through a cable network. And Changcho 21's model finally calculated the value of damage assessment with the results. In this paper, we develop an new expert system for close combat using decision tree. The experimental results show that the proposed expert system has similar performance to COBRA and has less computing complexity. And it can substitute for COBRA and be applicable to battlefield.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.405-408
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• 2016

Games like Go, Chess, Janggi have helped to brain development of the people. These games are developed by computer program. And many algorithms have been developed to allow myself to play. The person winning chess program was developed in the 1990s. But game of go is too large number of cases. So it was considered impossible to win professional go player. However, with the use of MCTS(Monte Carlo Tree Search) and CNN(Convolutional Neural Network), the performance of the go algorithm is greatly improved. In this paper, using CNN and MCTS were proceeding development of go algorithm. Using the manual of go learning CNN look for the best position, MCTS calculates the win probability in the game to proceed with simulation. In addition, extract pattern information of go using existing manual of go, plans to improve speed and performance by using it. This method is showed a better performance than general go algorithm. Also if it is receiving sufficient computing power, it seems to be even more improved performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5260-5275
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• 2019

In the process of constructing the traditional offensive and defensive game theory model, these are some shortages for considering the dynamic change of security risk problem. By analysing the critical indicators of the incomplete information game theory model, incomplete information attack and defense game theory model and the mathematical engineering method for solving Bayes-Nash equilibrium, the risk-averse income function for information assets is summarized as the problem of maximising the return of the equilibrium point. To obtain the functional relationship between the optimal strategy combination of the offense and defense and the information asset security probability and risk probability. At the same time, the offensive and defensive examples are used to visually analyse and demonstrate the incomplete information game and the Harsanyi conversion method. First, the incomplete information game and the Harsanyi conversion problem is discussed through the attack and defense examples and using the game tree. Then the strategy expression of incomplete information static game and the engineering mathematics method of Bayes-Nash equilibrium are given. After that, it focuses on the offensive and defensive game problem of unsafe information network based on risk aversion. The problem of attack and defense is obtained by the issue of maximizing utility, and then the Bayes-Nash equilibrium of offense and defense game is carried out around the security risk of assets. Finally, the application model in network security penetration and defense is analyzed by designing a simulation example of attack and defense penetration. The analysis results show that the constructed income function model is feasible and practical.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.529-534
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• 2021

Computing thinking is regarded as one of the important skills required in the 21st century, and many countries have introduced and implemented computing thinking training courses. Among computational thinking education methods, educational game-based methods increase student participation and motivation, and increase access to computational thinking. Autothinking is an educational game developed for the purpose of providing computational thinking education to learners. It is an adaptive system that dynamically provides feedback to learners and automatically adjusts the difficulty according to the learner's computational thinking ability. However, because the game was designed based on rules, it cannot intelligently consider the computational thinking of learners or give feedback. In this study, game data collected through Autothikning is introduced, and game score prediction that reflects computational thinking is performed in order to increase the adaptability of the game by using it. To solve this problem, a comparative study was conducted on linear regression, decision tree, random forest, and support vector machine algorithms, which are most commonly used in regression problems. As a result of the study, the linear regression method showed the best performance in predicting game scores.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.390-399
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• 2022

Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.179-184
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• 2019

This paper introduces a study for procedurally generating the behavior of objects in a virtual environment at runtime. This study was initiated to enable the behavioral model of objects in virtual reality applications to evolve in response to user behavior at runtime. Our approach is to describe the behavior of an object as a behavior tree, and to make a node of the behavior tree change to another type if a certain condition is satisfied. We defined four types of node changes: "parameterized", "probabilistic", "alternate", and "variant". We experimented with a virtual environment that includes a variety of simple procedural elements to explore the possibilities of our approach. As a result of the implementation, if an optimization algorithm that can select and apply the optimized procedural elements in response to the user's behavior is complemented, it is confirmed that more intelligent objects and agents can be implemented in virtual reality applications.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.283-288
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• 2019

In order to overcome the students' lack of information and experience, we developed a content planning tree that utilizes a decision tree. The content planning tree consists of a tree trunk creation step in which students select a theme and a story to develop, a parent branch generation step for selecting a category that can be developed based on the story, a child branch generation step for selecting the interesting "effect" method of producing the content effectively, a leaf generation step for selecting a multimedia expression 'element' to be visualized. The educational model was applied to game planning design and information visualization lectures, and provides examples of the categories, effects, and elements used in each lecture. The model was used for 145 team projects and the efficiency was confirmed by a step-by-step learning process.