• Asia pacific journal of information systems
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• v.32 no.3
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• pp.684-705
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• 2022

A new trend has been emerging in recent years, with video game live streaming becoming a meeting ground for gamers, as well as a marketing strategy for game developers. In line with this trend, the emergence of the "Let's Play" culture has significantly changed the manner in which people enjoyed video games. In order to academically explore this new experience, this study seeks to answer the following research questions: (1) Does engaging in video game streaming offer the same feeling as playing the game? (2) If so, what are the factors that affect the feeling of telepresence from viewers' perspective? and (3) How does the feeling of telepresence affect viewers' learning experience of the streamed game? We generated and empirically tested a comprehensive research model based on the telepresence and consumer learning theories. The research findings revealed that the authenticity and pleasantness of the streamer and the interaction of viewers positively affect telepresence, which in turn is positively associated with the gained knowledge and a positive attitude toward the streamed game. Based on the research findings, various practical implications are discussed for game developers as well as platform providers.

• Journal of Korea Game Society
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• v.15 no.2
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• pp.95-104
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• 2015

Many games for English vocabulary learning, such as hangman, cross puzzle, matching, etc, have been developed which are of board-type or computer game-type. Most of these computer games are adapting strategy-style game plays so that there is a limit on giving the fun, a nature of games, to learners who do not like games of this style. In this paper, a system for memorizing new English words is proposed which is based on a single-player car racing game targeting youths and adults. In the game, the core of our system, a learner drives a car and obtains game points by colliding with English word texts like game items appearing on the track. The learner keeps on viewing English words being exposed on the track while driving, resulting in memorizing those words according to a learning principle stating viewing is memorization. To our experiment, the effect of memorizing English words by our car racing game is good, and the degree of satisfaction with our system as a English vocabulary learning tool is reasonably high. Also, previous word games are suitable for the memory enforcement of English words but our game can be used for the memorization of new words.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.45-51
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• 2020

Recently, many games provide data related to the users' game play, and there have been a few studies that predict opponent move by combining machine learning methods. This study predicts opponent move using match data of a real-time strategy game named ClashRoyale and a multi-label classification based on machine learning. In the initial experiment, binary card properties, binary card coordinates, and normalized time information are input, and card type and card coordinates are predicted using random forest and multi-layer perceptron. Subsequently, experiments were conducted sequentially using the next three data preprocessing methods. First, some property information of the input data were transformed. Next, input data were converted to nested form considering the consecutive card input system. Finally, input data were predicted by dividing into the early and the latter according to the normalized time information. As a result, the best preprocessing step was shown about 2.6% improvement in card type and about 1.8% improvement in card coordinates when nested data divided into the early.

• 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.

• Journal of Convergence for Information Technology
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• v.9 no.5
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• pp.14-20
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• 2019

Energy saving is becoming more and more important issue due to lack of resources and limited nature. However, There is a lack of learning status on energy saving in the school field. In particular, in elementary education on energy saving was not linked to practice, and the educational effect was insufficient. Although various kinds of learning tools are utilized, many successful cases of energy saving game strategy are introduced in overseas industry field, and game design is proposed so that energy related education can be played through games. Because energy conservation can not be effective without practice, learning using games as a tool is expected to be more effective than learning based on knowledge transfer in the classroom. We propose a defense game for energy conservation education by using the mission elements, score acquisition element, time limit element, and character element which are the interesting elements of the game designed in the previous research.

• Journal of Korea Game Society
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• v.6 no.4
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• pp.25-37
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• 2006

Although the constructivistic instruction is emphasized in these days, many teachers feel burden to implement their teaching based on constructivistic teaming theory. As the main reason for this, difficulty to find out effective materials which can be utilized in the constructivistic instruction can be argued. According to recent studios, effectiveness of online games as the educational material was proven. Based on this result, the possibility for utilizing online games as effective educational materials in the constructivistic instruction was discussed in this study. For this, total process for the business strategy lecture implemented to 50 students in Chung-Ang univ. during 9 months by utilizing an online game named 'Goonzu' was reviewed by the perspective of constructivistic teaming theories. As a result of this review, it was proven that constructivistic methodologies were applied effectively in that lecture. Based on this, the possibility of utilizing online games as the effective material in the constructivistic teaming activity is discussed in this study.

• ETRI Journal
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• v.37 no.1
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• pp.77-87
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• 2015

In wireless networks, cooperation is necessary for many protocols, such as routing, clock synchronization, and security. It is known that cooperator nodes suffer greatly from problems such as increasing energy consumption. Therefore, rational nodes have no incentive to cooperatively forward traffic for others. A rational node is different from a malicious node. It is a node that makes the best decision in each state (cooperate or non-cooperate). In this paper, game theory is used to analyze the cooperation between nodes. An evolutionary game has been investigated using two nodes, and their strategies have been compared to find the best one. Subsequently, two approaches, one based on a genetic algorithm (GA) and the other on learning automata (LA), are presented to incite nodes for cooperating in a noisy environment. As you will see later, the GA strategy is able to disable the effect of noise by using a big enough chromosome; however, it cannot persuade nodes to cooperate in a noisefree environment. Unlike the GA strategy, the LA strategy shows good results in a noise-free environment because it has good agreement in cooperation-based strategies in both types of environment (noise-free and noisy).

• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.161-168
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• 2020

Predicting outcome of the sports enables teams to establish their strategy by analyzing variables that affect overall game flow and wins and losses. Many studies have been conducted on the prediction of the outcome of sports events through statistical techniques and machine learning techniques. Predictive performance is the most important in a game prediction model. However, statistical and machine learning models show different optimal performance depending on the characteristics of the data used for learning. In this paper, we propose a new ensemble model to predict English Premier League soccer games using statistical models and the machine learning models which showed good performance in predicting the results of the soccer games and this model is possible to select a model that performs best when predicting the data even if the data are different. The proposed ensemble model predicts game results by learning the final prediction model with the game prediction results of each single model and the actual game results. Experimental results for the proposed model show higher performance than the single models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3867-3886
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• 2015

This article investigates the problem of opportunistic spectrum access in dynamic environment, in which the signal-to-noise ratio (SNR) is time-varying. Different from existing work on continuous feedback, we consider more practical scenarios in which the transmitter receives an Acknowledgment (ACK) if the received SNR is larger than the required threshold, and otherwise a Non-Acknowledgment (NACK). That is, the feedback is discrete. Several applications with different threshold values are also considered in this work. The channel selection problem is formulated as a non-cooperative game, and subsequently it is proved to be a potential game, which has at least one pure strategy Nash equilibrium. Following this, a multi-agent Q-learning algorithm is proposed to converge to Nash equilibria of the game. Furthermore, opportunistic spectrum access with multiple discrete feedbacks is also investigated. Finally, the simulation results verify that the proposed multi-agent Q-learning algorithm is applicable to both situations with binary feedback and multiple discrete feedbacks.

• International Journal of Contents
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• v.7 no.4
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• pp.10-18
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• 2011

Cooperative Group Games (CGG) emphasize participation, challenges and fun by cooperation among group members rather than competition. CGGs have been proven to be an efficient education method that teaches the value of cultivating cooperation skills, discipline, and sense of public order for tackling problems together through various types of interactions. When integrated with computer game technology, the general classic CGG can be reborn with new educational and entertaining aspects. To combine the joy of physical movement of group games and the richness of computer game contents, a motion based arcade CGG has been developed in this study, based on the original ideas and structures of classic off-line CGGs. While implementing the classic game concepts in arcade environment, various design attributes have been considered and applied, which were supposed to promote cooperative game play. Overall, the process of the implementation and test results of our four CGGs suggest several design strategies for effective arcade CGGs.