• Korean Journal of Culture and Social Issue
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• v.15 no.4
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• pp.565-589
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• 2009

The present study explored the major components of adaptive game behavior among adolescents in Korea. Based on relevant research and a pilot testing, an Adaptive Game Use Scale (AGUS) was developed and validated. A stratified sampling procedure was used to draw a representative sample, and a total of 600 male and female students from middle schools and high schools in various regions participated in the study. Factor analyses revealed 7 facets of adaptive game behavior, including experiencing vitality, expanding life experience, making good use of leisure time, experiencing flow, exercising control, experiencing self-esteem, maintaining and expanding social network. Internal consistency and temporal stability(4 weeks) of the scale were both high. A confirmatory factor analysis indicated that a 7-factor hierarchical model fits well with the data. Moreover, additional analyses suggested that AGUS and game addiction are conceptually distinct. Correlational analyses also indicated that AGUS has good discriminant validity and concurrent validity. Implications of the findings and future directions were discussed.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.404-419
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• 2017

This study is to examine how middle and high school students vary in terms of good and bad usage of online games and how these factors have varying effects on their use of the internet as a whole. My focus is to study their behavioral patterns individually while playing internet-based online games. The results are as follows: First, 260 out of 390 subjects used the internet. Male students who are high school students with siblings, or preschool time game users were revealed to play online games more often rather than female students who are middle school students with no siblings, or non-preschool time game users. Secondly, the analysis of differences of good and bad usage of online games revealed that there is a significant correlation between gender and beginning age. Lastly, a thorough analysis of the average difference in terms of following the online game shutdown found that there is no significant correlation among the sub-groups. However, an analysis of the difference of the problematic game usage has shown that there is a significant difference in the heavy user group. This findings means that the students who don't follow the online game shutdown spend more time than those who do.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.6069-6091
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• 2017

Augmented Reality (AR) overlays virtual information on real world data, such as displaying useful information on videos/images of a scene. This paper presents an Enhanced AR (EAR) system that displays useful statistical players' information on captured images of a sports game. We focus on the situation where the input image is degraded by strong sunlight. Proposed EAR system consists of an image enhancement technique to improve the accuracy of subsequent player and face detection. The image enhancement is followed by player and face detection, face recognition, and players' statistics display. First, an algorithm based on multi-scale retinex is proposed for image enhancement. Then, to detect players' and faces', we use adaptive boosting and Haar features for feature extraction and classification. The player face recognition algorithm uses boosted linear discriminant analysis to select features and nearest neighbor classifier for classification. The system can be adjusted to work in different types of sports where the input is an image and the desired output is display of information nearby the recognized players. Simulations are carried out on 2096 different images that contain players in diverse conditions. Proposed EAR system demonstrates the great potential of computer vision based approaches to develop AR applications.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.139-146
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• 2002

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless these algorithms can learn the optimal policy if the agent can visit every state-action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem, we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL) as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state space effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. In this paper we use the AMMQL algorithn as a learning method for dynamic positioning of the robot soccer agent, and implement a robot soccer agent system called Cogitoniks.