• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.663-676
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• 2016

In Korean professional baseball the championship team of the year is determined by the four series of games: semi-semi-playoff, semi-playoff, playoff and korean series. To the top 5 teams in a regular season privileges are given to play the games at post season. At semi-semi playoff the winner of two teams which are ranked at 4th and 5th place in the regular season can advance to the game of semi playoff. The winner at semi playoff advances to the playoff to play with the second place team in the regular season. Finally, the championship team is to be determined in the Korean series between the winner of the playoff and the first ranked team in the regular season. We propose methods of how to calculate the winning probabilities of each of high ranked 5 teams advancing to Korean series. From our proposed methods we can estimate the championship probabilities of each of high ranked 5 teams advancing to the Korean series only if we know the winning probabilities between two teams in the regular season or the post season.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.690-693
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• 2003

The purpose of two player game is that a player beats an enemy. In order to win to various enemies, a learning of various strategies is indispensable. However, the optimal action to overcome the enemies will change when the game done over and again because the enemy's actions also change dynamically. Sol it is din-cult that the player aquires the optimal action and that the specific player keeps winning to various enemies. Species who have a competition relation and affect other's existence is called a coevolution. Coevolution has recently attracred considerable interest in the community of Artificial Life and Evolutionary Computation(1). In this paper, we apply Classifier System for agent team to two player game. A reward and a penalty are given to the used rules when the agent achieve specific action in the game and each team's rulebase are evaluated based on the ranking in the league. We show that all teams can acquire the optimal actions by coevolution.

• Journal of Korea Game Society
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• v.4 no.2
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• pp.13-20
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• 2004

We have been interested in solving escapee-chaser game. In this game, with avoiding chaser, the escapee must escape from given male. The winning strategies of the escapee are driving the chaser to an intended place and closely evading from chaser by using some walls. According to our experience, some stages of the game are too difficult to solve manually. So we take the model checking method to get a solution of the game. Because the model checking with breadth fist search manner exhaustively searches the all state space of the game, the solution using model checking is best solution, shortest path. Fortunately, during the process of finding solution path, the state space explosion problem didn't occur, and the results of the game solving was applied to embedded system, Lego Mindstorm. Two agents, escapee and chaser, were implemented into robots and several experiments conformed the correctness of our solution.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.289-295
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• 2013

Arcade game up until early 2000 had been highly influential and profitable industry that occupied most of the market share for the Korean domestic game industry. However, extensive and yet tightened restrictions were imposed to the industry in 2006 due to the problems of speculative games such as winning rate manipulation, serious addictiveness, and etc. This resulted in damaging healthy arcade game business and eventually caused it seemingly impossible to comeback up until now. This study is to address the current arcade game industry in Korea along with the best practices in other countries to come up with propositions for the comeback of domestic arcade game industry and improvement of competitiveness. Ultimately it is to develop diversification and flexibility and to induce new business opportunities in the domestic arcade game industry by proposing the ways to convert the bad perception and introduction of functional games.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.543-552
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• 2012

Parrondo paradox is a counter-intuitive phenomenon where two losing games can be combined to win or two winning games can be combined to lose. When we trade stocks with a history-dependent Parrondo game rule (where we buy and sell stocks based on recent investment outcomes) we found Parrondo paradox in stock trading. Using stock data of the KRX from 2008 to 2010, we analyzed the Parrondo paradoxical cases in the Korean stock market.

• 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 Korean Institute of Industrial Engineers
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• v.42 no.1
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• pp.65-72
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• 2016

Curling is compared to the Chess because of variety and importance of strategies. For winning the Curling game, selecting optimal strategies at decision making points are important. However, there is lack of research on optimal strategies for Curling. 'Aggressive' and 'Conservative' strategies are common strategies of Curling; nevertheless, even those two strategies have never been studied before. In this study, Markov Decision Process would be applied for Curling strategy analysis. Those two strategies are defined as actions of Markov Decision Process. By solving the model, the optimal strategy could be found at any in-game states.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.973-982
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• 2009

Two losing games that can be combined, either by periodic alternation or by random mixture, to form a winning game are known as Parrondo games. We consider a collective version of Parrondo games in which players are allowed to choose the game to be played by the whole ensemble in each turn. In this paper, we analyze the long-range optimization strategy for all choices of the parameters and find the expected average profit in the steady state.

• 한국체육학회지인문사회과학편
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• v.57 no.4
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• pp.387-394
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• 2018

The purpose of this study is to provide information regarding the strategies by identifying the main variables that determines the winning team based on the records of all games of the 2017 IIHF World Championship Top league. 64 matches were analyzed for the study. 6 variables were analyzed which included ratio of saves, shots on goal, penalties in minutes, time for power play, power play goals, and face off wins. Logistic regression analysis (LRA), multiple regression analysis (MRA), and principal component analysis (PCA) were implemented to examine the relationship between win and loss. In case of LRA, shots on goal (p<.001), face-off wins (p<.001) had significantly positive relation to winning of game whereas, penalties in minutes (p<.01) and time on power play (p<.01) had significantly negative. Using MRA, win percentage was calculated which had significant positive correlation to ratio of saves (p<.01) and face-off wins (p<.001) whereas, a significant negative with penalties in minutes (p<.001). For PCA, the winning team consisted of penalty, attack, and defense factors whereas, losing teams consisted only the attack and defense factors.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.8-17
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• 2014

In this research, we employed various data mining techniques to build predictive models for win-loss prediction in Korean professional baseball games. The historical data containing information about players and teams was obtained from the official materials that are provided by the KBO website. Using the collected raw data, we additionally prepared two more types of dataset, which are in ratio and binary format respectively. Dividing away-team's records by the records of the corresponding home-team generated the ratio dataset, while the binary dataset was obtained by comparing the record values. We applied seven classification techniques to three (raw, ratio, and binary) datasets. The employed data mining techniques are decision tree, random forest, logistic regression, neural network, support vector machine, linear discriminant analysis, and quadratic discriminant analysis. Among 21(= 3 datasets${\times}$7 techniques) prediction scenarios, the most accurate model was obtained from the random forest technique based on the binary dataset, which prediction accuracy was 84.14%. It was also observed that using the ratio and the binary dataset helped to build better prediction models than using the raw data. From the capability of variable selection in decision tree, random forest, and stepwise logistic regression, we found that annual salary, earned run, strikeout, pitcher's winning percentage, and four balls are important winning factors of a game. This research is distinct from existing studies in that we used three different types of data and various data mining techniques for win-loss prediction in Korean professional baseball games.