• Journal of Korea Game Society
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• v.8 no.4
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• pp.3-16
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• 2008

This study tried to look into various linguistic elements of MMORPG and suggested teaching methods to be applied in teaching and learning English. To support this idea, diverse attributes of CALL applications were investigated and suggested suitable position of MMORPG in technological stream of CALL. After that, focusing on 'Goonzu Global', the linguistic environment of MMORPG was considered. Based on aforementioned results, this study proposed an effective method to utilize MMORPG in teaching English adopting three teaching methods; collaborative method, task-based learning and problem-based learning.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.39-46
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• 2020

Recently, for designing virtual characters in the battle game field effectively, some methods are very needed to predicate the win rates of the battle of them efficiently. In this paper, we propose a method to solve this problem by combining simulation and machine learning. Firstly, a simulation is used to analyze the win rates of the battle of virtual characters in the battle game. In addition, we apply a regression model based machine learning scheme to predict win rates of the battle of virtual characters according to their abilities. Our experimental results using suggested method show that it is almost no difference between the win rates of the simulation and the prediction results using the machine learning scheme. And also, we can obtain good performance in the experiment using only simple regression based machine learning model.

• Journal of Korea Game Society
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• v.16 no.2
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• pp.27-40
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• 2016

Predicting stealthy behaviors plays an important role in designing stealth games. It is, however, difficult to automate this task because human players interact with dynamic environments in real time. In this paper, we present a reinforcement learning (RL) method for simulating stealthy movements in dynamic environments, in which an integrated model of Q-learning with Artificial Neural Networks (ANN) is exploited as an action classifier. Experiment results show that our simulation agent responds sensitively to dynamic situations and thus is useful for game level designer to determine various parameters for game.

• 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.

• The Journal of Korean Association of Computer Education
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• v.11 no.6
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• pp.95-102
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• 2008

In this paper through the implementation of cooperative learning using SDG, education puzzle actually applies to computer training. SDG(Single Display Groupware) which one computer display have a multi-input devices can work as a collaborative system. Learners are performing together through SDG-based cooperative learning system. SDG cooperative learning with a multi-input device is superior to traditional learning with individual. We have implementation of the puzzle game with this fact. This system through effective education and raising their children's education participation rate will be able to do.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.23-32
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• 2023

E-sports, a burgeoning facet of modern sports culture, has achieved global prominence. Particularly, Aeon of Strife (AOS) games, emblematic of E-sports, blend individual player prowess with team dynamics to significantly influence outcomes. This study aggregates and analyzes real user gameplay data using statistical techniques. Furthermore, it develops and tests win-loss prediction models through machine learning, leveraging a substantial dataset of 1,149,950 individual data points and 230,234 team data points. These models, employing five machine learning algorithms, demonstrate an average accuracy of 80% for individual and 95% for team predictions. The findings not only provide insights beneficial to game developers for enhancing game operations but also offer strategic guidance to general users. Notably, the team-based model outperformed the individual-based model, suggesting its superior predictive capability.

• Journal of the Korea Convergence Society
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• v.1 no.1
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• pp.1-15
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• 2010

The Education has responsibility for predicting the social changes and cultivating global talent which the society needs. The ministry of education, science and technology in govern ment has been the concerns on social educational changes and thus built the '5 31 educational reform policy' in 1995 by the educational reform committee. As a solution of a social change, this paper reviews the three-phase educational information policies, and e-learning and u-learning which are the main technologies in educational information. Also, the technologies of e-learning can be divided into m-learning, t-learning, u-learning, r-learning, game-based learning according to the contents mass media. Among them, this paper introduces the concept of robot-learning, called r-learning, and compares it with u-learning.

• Proceedings of the Korean Information Science Society Conference
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• 2010.11a
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• pp.80-81
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• 2010

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.61-66
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• 2024

This paper aims to design a simulation for path-finding of objects in a simulation or game environment using reinforcement learning techniques. The main feature of this study is that the objects in the simulation are trained to avoid obstacles at random locations generated on a given track and to automatically explore path to get items. To implement the simulation, ML Agents provided by Unity Game Engine were used, and a learning policy based on PPO (Proximal Policy Optimization) was established to form a reinforcement learning environment. Through the reinforcement learning-based simulation designed in this study, we were able to confirm that the object moves on the track by avoiding obstacles and exploring path to acquire items as it learns, by analyzing the simulation results and learning result graph.

• Journal of Korea Game Society
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• v.1 no.1
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• pp.61-67
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• 2001

This paper describes a program which learns good strategies for two-poison, deterministic, zero-sum board games of perfect information. The program learns by simply playing the game against either a human or computer opponent. The results of the program's teaming of a lot of games are reported. The program consists of search kernel and a move generator module. Only the move generator is modified to reflect the rules of the game to be played. The kernel uses a temporal difference procedure combined with a backpropagation neural network to team good evaluation functions for the game being played. Central to the performance of the program is the search procedure. This is a the capture tree search used in most successful janggi playing programs. It is based on the idea of using search to correct errors in evaluations of positions. This procedure is described, analyzed, tested, and implemented in the game-teaming program. Both the test results and the performance of the program confirm the results of the analysis which indicate that search improves game playing performance for sufficiently accurate evaluation functions.