• Journal of the military operations research society of Korea
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• v.30 no.1
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• pp.70-80
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• 2004

In discrete red and black, you can stake any amount s in your possession, but the value of s takes positive integer value. Suppose your goal is N and your current fortune is f, with 0＜f＜N. You win back your stake and as much more with probability p and lose your stake with probability, q = 1-p. In this study, we consider optimum strategies for this game with the value of p greater than $\frac{1}{2}$ where the player has the advantage over the house. The optimum strategy at any f when p>$\frac{1}{2}$ is to play timidly, which is to bet 1 all the time. This is called as Timid1 strategy. In this paper, we perform the simulation study to show that the Timid1 strategy is optimum in discrete red and black when p＞\frac{1}{2}.

• Journal of the military operations research society of Korea
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• v.31 no.1
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• pp.122-129
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• 2005

In discrete red and black, you can stake any amount s in your possession, but the value of s takes positive integer value. Suppose your goal is N and your current fortune is ${\Large\;f},\;with\;O<{\Large\;f}. You win back your stake and as much more with probability p and lose your stake with probability, q=1-p. In this study, we consider optimum strategies for this game with the value of p less than ${\frac{1}{2}}$ where the house has the advantage over the player. It is shown that the optimum strategy at any ${\Large\;f}$ is the DBold strategy which is to play boldly in discrete red and black when $p<{\frac{1}{2}}$. And then, we perform the simulation study to show that this strategy, which is to bet as much as you can, is optimal in discrete case.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1299-1305
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• 2017

In this paper, we consider a beamforming game between the transmitters in a two-user multiple-input single-output interference channel using limited feedback and investigate how each transmitter is able to find a modified strategy from the quantized channel state information (CSI). In the beamforming game, each of the transmitters (i.e., a player) tries to maximize the achievable rate (i.e., a payoff function) via a proper beamforming strategy. In our case, each transmitter's beamforming strategy is represented by a linear combining factor between the maximum ratio transmission (MRT) and the zero forcing (ZF) beamforming vectors, which is the Pareto optimal achieving strategy. With the quantized CSI, the transmitters' strategies may not be valid because of the quantization errors. We propose a modified solution, which takes into account the effects of the quantization errors.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.491-496
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• 2002

Multi-objective Optimization Problems(MOPs) are occur more frequently than generally thought when we try to solve engineering problems. In the real world, the majority cases of optimization problems are the problems composed of several competitive objective functions. In this paper, we introduce the definition of MOPs and several approaches to solve these problems. In the introduction, established optimization algorithms based on the concept of Pareto optimal solution are introduced. And contrary these algorithms, we introduce theoretical backgrounds of Nash Genetic Algorithm(Nash GA) and Evolutionary Stable Strategy(ESS), which is the basis of Co-evolutionary algorithm proposed in this paper. In the next chapter, we introduce the definitions of MOPs and Pareto optimal solution. And the architecture of Nash GA and Co-evolutionary algorithm for solving MOPs are following. Finally from the experimental results we confirm that two algorithms based on Evolutionary Game Theory(EGT) which are Nash GA and Co-evolutionary algorithm can search optimal solutions of MOPs.

• Journal of Korea Game Society
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• v.15 no.1
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• pp.89-100
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• 2015

This study aims to figure out the impact of cross-promotion strategy on flow experience to overcome the restrictions of social network game. Social network games faces tougher market competition and shorter product life-cycles. The results show that the less users play games, the more they feel flow experience led by interests and self-expression of SNG. On the contrary, the more they play games, self- expression and sense of competition factor are proved to be effective factor for flow. Also Users' cognition for usefulness and annoyingness of cross- promotion are different according to level of game uses and promotion uses. People who play games more and utilize promotion more appreciate the usefulness of promotion and indulge in flow experience of SNG.

• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.9-15
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• 2009

It takes quite amount of time to study a board game because there are many game characters and different stages are exist for board games. Also, the opponent is not just a single character that means it is not one on one game, but group vs. group. That is why strategy is needed, and therefore applying optimum learning is a must. This paper used reinforcement learning algorithm for board characters to learn, and so they can move intelligently. If there were equal result that both are considered to be best ones during the course of learning stage, Heuristic which utilizes learning of problem area of Jul-Gonu was used to improve the speed of learning. To compare a normal character to an improved one, a board game was created, and then they fought against each other. As a result, improved character's ability was far more improved on learning speed.

• The Journal of the Korea Contents Association
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• v.17 no.6
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• pp.203-224
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• 2017

With the development of the IT industry and the growth of the cultural industry, the game industry is becoming an important industry. In this regard, the study seeks to differentiate the generation of video games based on technological characteristics from the perspective of technological innovation. SEGA, Nintendo, MicroSoft, SONY, and ATARI were chosen as research subjects. The survy was conducted from ATARI to 2017. The results of the study are expected to help develop the technology strategy of the future video game industry.

• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.247-255
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• 2004

The majority of real-world problems encountered by engineers involve simultaneous optimization of competing objectives. In this case instead of single optima, there is a set of alternative trade-offs, generally known as Pareto-optimal solutions. The use of evolutionary algorithms Pareto GA, which was first introduced by Goldberg in 1989, has now become a sort of standard in solving Multiobjective Optimization Problems (MOPs). Though this approach was further developed leading to numerous applications, these applications are based on Pareto ranking and employ the use of the fitness sharing function to maintain diversity. Another scheme for solving MOPs has been presented by J. Nash to solve MOPs originated from Game Theory and Economics. Sefrioui introduced the Nash Genetic Algorithm in 1998. This approach combines genetic algorithms with Nash's idea. Another central achievement of Game Theory is the introduction of an Evolutionary Stable Strategy, introduced by Maynard Smith in 1982. In this paper, we will try to find ESS as a solution of MOPs using our game model based co-evolutionary algorithm. First, we will investigate the validity of our co-evolutionary approach to solve MOPs. That is, we will demonstrate how the evolutionary game can be embodied using co-evolutionary algorithms and also confirm whether it can reach the optimal equilibrium point of a MOP. Second, we will evaluate the effectiveness of our approach, comparing it with other methods through rigorous experiments on several MOPs.

• Journal of The Korean Association of Information Education
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• v.12 no.1
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• pp.99-107
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• 2008

Over online education based on wired Internet technologies, due to recent development of various mobile technologies, education based on mobile environment becomes popular. In the meanwhile, the young students are more interested in game-based education that provides more interaction and instant feedback than one-way cramming education. The purpose of this thesis is to develop a new motivation model for mobile environment and apply the model to the elementary school students. The proposed model, based on Keller's motivation model, is designed to increase study effects through motivating students with various game strategies. The proposed motivation model has the following characteristics. First of all, the best game genre can be provided for each study theme in early planning stage. Second, the model can allow students to have more interests in their study activity by providing various edutainment elements. Third, a stage of producing game synopsis and concrete scenario is included in motivation model. The stage enables more complete combination of game and mobile motivation strategies. Finally, the proposed model allows contents developed to be appled in teaching plan without any refinement. That is, the model allows a teaching plan to be extracted from study contents instantly.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.278-286
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• 2020

The prisoner's dilemma game which is a representative example of game theory is being studied with interest by many economists, social scientists, and computer scientists. In recent years, many researches on computational approaches that apply evolutionary computation techniques such as genetic algorithms and particle swarm optimization have been actively conducted to analyze prisoner dilemma games. In this study, we intend to evolve a strategy for a iterated prisoner dilemma game participating two or more players using three different binary particle swarm optimization techniques. As a result of experimenting by applying three kinds of binary particle swarm optimization to the iterated prisoner's dilemma game, it was confirmed that mutual cooperation can be established even among selfish participants to maximize their own gains. However, it was also confirmed that the more participants, the more difficult to establish a mutual cooperation relationship.