• Korean Journal of Cognitive Science
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• v.21 no.3
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• pp.429-446
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• 2010

This study is to examine if emotional valance depending upon the result of baseball game(losing or winning) of subjects' favorite team yields hemispheric asymmetry measured by fMRI. Subjects were twelve fans of the Samsung Lions baseball team. The brain activations have been observed while they watched winning and losing scenes of their favorite team. As a results of the experiment, those who watched winning scenes showed the activation of the left and right cuneus, right inferior occipital gyrus, right inferior frontal gyrus, left amygdala, right parahippocampal gyrus, left uncus, left cingulate gyrus, left inferior temporal gyrus, right middle temporal gyrus, left declive, left culmen. On the contrary, those who watched losing scenes showed the activation in the right middle frontal gyrus, left anterior cingulate, left sub-gyral, left lentifomrm nucleus, left thalamus, left claustrum, left insula. The evidence of hemispheric asymmetry from this study has not been demonstrated and activation in amygdala observed during watching winning scene has not been observed in losing scene. Therefore more in-dept research is required about defeat stimuli induction.

• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.539-549
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• 2008

Most important thing in professional baseball game among all kinds of sports is the winning. Both coaches and players collected and analyzed lots of game data to get a victory. In this paper, batting data are analyzed so as to represent informations of hitting direction visually. This method could be provided a lot of useful information about hitting direction of a specific batter or a team to not only coaches, players but also the audience.

• International journal of advanced smart convergence
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• v.4 no.2
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• pp.154-162
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• 2015

This paper focuses on how cognitive and emotional structures affect humans during long-term interaction. We design an interaction with a turn-based game, the Chopstick Game, in which two agents play with numbers using their fingers. While a human and a robot agent alternate turn, the human user applies herself to play the game and to learn new winning skills from the robot agent. Conventional valence and arousal space is applied to design emotional interaction. For the robotic system, we implement finger gesture recognition and emotional behaviors that are designed for three-dimensional virtual robot. In the experimental tests, the properness of the proposed schemes is verified and the effect of the emotional interaction is discussed.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.135-142
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• 2020

Basketball is a highly complex sport, analyses offensive and defensive rebounds, free throw percentages, minutes played and an efficiency rating. These statistics can have a large bearing and provide a lot of pressure on players as their every move can be analysed. Performance analysis in sport is a vital way of being able to track a team or individuals performance and more commonly used resource for player and team development. Discovering information such as this proves the importance of these types of analysis as with post competition video analysis a coach can reach a far more accurate analysis of the game leading to the ability to coach and correct the exact requirements of the team instead of their perceptions. A significant difference was found between winning and losing performances for different types of turnovers supporting current research that states that turnovers are not a valid predictor of match outcomes and that there is no specific type of turnover which can predict the outcome of a match as briefly mentioned in Curz and Tavares (1998). Significant differences were found between winning and tied and losing and tied performance for some types of turnovers, however due to the lack of data collected in this area they cannot be considered valid. Further research could also be conducted in other areas relating to performance indicators where there is currently minimal research in some areas such as assisted baskets, stated about the performance indicators in their own study the performance indicators are inadequate for explaining the complexities of the game suggesting that one indicator will not be constant in every game an research into performance analysis areas would be more appropriate.

• Journal of IKEEE
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• v.23 no.4
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• pp.1187-1194
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• 2019

This paper proposes a CNN architecture as value function network of an artificial intelligence Othello game agent and its learning scheme using reinforcement learning algorithm. We propose an approach to construct the value function network by using CNN to learn the records of professional players' real game and an approach to enhance the network parameter by learning from self-play using reinforcement learning algorithm. The performance of value function network CNN was compared with existing ANN by letting two agents using each network to play games each other. As a result, the winning rate of the CNN agent was 69.7% and 72.1% as black and white, respectively. In addition, as a result of applying the reinforcement learning, the performance of the agent was improved by showing 100% and 78% winning rate, respectively, compared with the network-based agent without the reinforcement learning.

• Journal of Korea Game Society
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• v.21 no.4
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• pp.117-130
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• 2021

The purpose of this paper is to study users' intention on paid game items through language network analysis. The functional item buying group considered the capabilities and strength of the game character as the most important factor for the winning of the games while the decorative game item buying users considered self-satisfaction for the unique outfit of their game character. Moreover, they reflected their game characters as themselves and another ego.

• Asia pacific journal of information systems
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• v.16 no.4
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• pp.103-119
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• 2006

One of the most important reasons for using online game is intrinsic motivation which relates the drive to perform a behavior for perceptions of pleasure and satisfaction from the behavior itself. Therefore, most studies for using online game have been based on theories for intrinsic motivation such as flow. However, such theories have some limitations for explaining social aspects of online games such as Massively Multiplayer Online Role Game (MMORPG), which provides a society for game players by using a virtual space for playing games so called 'persistent world'. Adaptive structuration theory (AST) is one of trying to capture the change of using IT due to social interactions between users and system. In order to explain online game players' behavior considering social interaction, we suggest a research model based on AST. Our model aims to investigate the relationship between appropriation which is determined by faithfulness of appropriation (FOA) and consensus on appropriation (COA) and performance which is represented by the relationship among perceived winning, flow, and intention of reuse. An empirical test of our model for 125 MMORPG users which is analyzed by Partial Least Square method shows very satisfactory and interesting results. While hypotheses suggested in our model are supported, the influence of COA on game performance is shown to be stronger than that of FOA.

• Journal of Korea Game Society
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• v.14 no.6
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• pp.39-48
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• 2014

Go is an extremely complex strategy board game despite its simple rules. By using MCTS, the computer Go programs with handicap game have been defeated human Go professionals. MCTS is based on the winning rate estimated by MC simulation rather than strategy concept. Meanwhile Genetic algorithm equipped with an adequate fitness function can find out the best solutions in the game. The game of Tic-Tac-Toe, also known as Naughts and Crosses, is one of the most popular games. We tried to find out the best strategy in the game of Tic-Tac-Toe. The experimental result showed that Genetic algorithm enables to find efficient strategies and can be applied to other board games such as Go and chess.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.579-590
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• 2020

We would use a coin or a game of Rock-Paper Scissors before the a main game to determine which team will begin first. We can use the game of Rock-Paper Scissors to choose one of two or one of many. There can be many rules in a game of Rock-Paper Scissors between two teams and two teams may consist of a different number of players. In this paper, we find the means and variances of the total number of games till the winning team is decided according to different game rules.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.31-37
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• 2017

There are lots of combined battlefield elements which complete the war. It looks problematic when collecting and analyzing these elements and then predicting the situation of war. Commander's experience and military power assessment have widely been used to come up with these problems, then simulated combat training program recently supplements the war-game models through recording real-time simulated combat data. Nevertheless, there are challenges to assess winning factors of combat. In this paper, we characterize the combat element (ce) by clustering simulated combat data, and then suggest multi-layered artificial neural network (ANN) model, which can comprehend non-linear, cross-connected effects among ces to assess mission completion degree (MCD). Through our ANN model, we have the chance of analyzing and predicting winning factors. Experimental results show that our ANN model can explain MCDs through networking ces which overperform multiple linear regression model. Moreover, sensitivity analysis of ces will be the basis of predicting combat situation.