• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.680-689
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• 2013

In recent, much attention has been paid to the educational serious games that provide both fun and learning effects. However, most educational games have been targeted at the infants and children, and it is still hard to use such games in higher education. On the contrary, this paper aims to develop an educational game for teaching mathematical programming to the undergraduates. It is well known that most puzzle games can be transformed into associated optimization problem and vice versa, and this paper proposes a simple educational game based on the subset sum problem. This game enables the users to play the puzzle and construct their own mathematical programming model for solving it. Moreover, the users are provided with appropriate instructions for modeling and their models are evaluated by using the data automatically generated. It is expected that the educational game in this paper will be helpful for teaching basic programming models to the students in industrial engineering or management science.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.3
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• pp.172-179
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• 2006

Over the past few years, a considerable number of studies have been conducted on modeling the bargaining game using artificial agents on within-model interaction. However, very few attempts have been made at study on between-model interaction. This paper investigates the interaction and co-evolutionary process among heterogeneous artificial agents in the bargaining game. We present two kinds of the artificial agents participating in the bargaining game. They play some bargaining games with their strategies based on genetic algorithm (GA) and reinforcement learning (RL). We compare agents' performance between two agents under various conditions which are the changes of the parameters of artificial agents and the maximal number of round in the bargaining game. Finally, we discuss which agents show better performance and why the results are produced.

• Fourth Industrial Review
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• v.4 no.1
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• pp.11-18
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• 2024

Purpose: The purpose of this study is to develop a geography class teaching and learning guide that enables learners to realistically explore the characteristics of the world's climate and geographical environment using digital tools. Research design, data and methodology: We review previous research on classes using goal-based scenario learning models, gamification, and digital tools, and explore tools that can be applied to world geography classes. Based on the exploration results, a goal-based scenario learning module is designed and a strategy for promoting educational gamification is established based on the ADDIE instructional design model. Results: The study comprises four sessions. Sessions 1-3 involve performance evaluations using a goal-based scenario learning module. Learners create game characters reflecting geographical characteristics, present results, and proceed with 3D modeling. In Session 4, a gamification class using Google Sites on the CoSpaces metaverse platform will be conducted. Conclusions: The study introduces a goal-based scenario learning model and a gamification class using digital tools to empower learners in exploring geographical diversity and its impact on lifestyles. Utilizing an accessible online platform, the study provides practical measures for integrating digital tools into geography education, addressing the current importance of digital technology in teaching.

• Journal of Korea Game Society
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• v.14 no.4
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• pp.105-120
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• 2014

Many of recent studies have suggested various positive outcomes of serious games. However, relatively little emphasis has been placed on the roles of user-centered factors from a psychological perspective. One of the main goals of serious games is the change of the user's perception and behavior towards a positive direction. To achieve this goal, psychological factors should be applied to the user's playing process in serious games. Inspired by construal-level theory(CLT), we propose a CLT applied model (CLT in process-outcome serious games model) considering psychological factors on the player's decision making. The model will be useful not only to game developers or designers but also to game researchers as a valuable tool in persuasion and learning for serious game users.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.769-782
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• 2022

A study on agricultural automation became more important. In Korea, sweet persimmon farmers spend a lot of time and effort on classifying profitable persimmons. In this paper, we propose and implement an efficient grading algorithm for persimmons before shipment. We gathered more than 1,750 images of persimmons, and the images were graded and labeled for classifications purpose. Our main algorithm is based on EfficientDet object detection model but we implemented more exquisite method for better classification performance. In order to improve the precision of classification, we adopted a machine learning algorithm, which was proposed by PyCaret machine learning workflow generation library. Finally we acquired an improved classification model with the accuracy score of 81%.

• Journal of Korea Game Society
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• v.21 no.6
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• pp.75-86
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• 2021

Brand image refers to how customers, stakeholders and the market see and recognize the brand. A positive brand image leads to continuous purchases, but a negative brand image is directly linked to consumers' buying behavior, such as stopping purchases, so from the corporate perspective, it needs to be quickly and accurately identified. Currently, methods of investigating brand images include surveys and SNS surveys, which have limited number of samples and are time-consuming and costly. Therefore, in this study, we are going to conduct an emotional analysis of text data on social media by utilizing the machine learning based KoBERT model, and then suggest how to use it for game corporate brand image analysis and verify its performance. The result has proved some degree of usability showing the same ranking within five brands when compared with the BRI Korea's brand reputation ranking.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.143-152
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• 2021

It is very important to learn behavioral policies that allow multiple agents to work together organically for common goals in various real-world applications. In this multi-agent reinforcement learning (MARL) environment, most existing studies have adopted centralized training with decentralized execution (CTDE) methods as in effect standard frameworks. However, this multi-agent reinforcement learning method is difficult to effectively cope with in a dynamic environment in which new environmental changes that are not experienced during training time may constantly occur in real life situations. In order to effectively cope with this dynamic environment, this paper proposes a novel multi-agent reinforcement learning system, C-COMA. C-COMA is a continual learning model that assumes actual situations from the beginning and continuously learns the cooperative behavior policies of agents without dividing the training time and execution time of the agents separately. In this paper, we demonstrate the effectiveness and excellence of the proposed model C-COMA by implementing a dynamic mini-game based on Starcraft II, a representative real-time strategy game, and conducting various experiments using this environment.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.131-135
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• 2024

Baseball attracts the largest audience among professional sports in Korea. In particular, attendance is the primary source of income in baseball. Previous studies have limitations in reflecting the characteristics of individual stadium. For instance, the KIA Tigers exhibit the highest away game revenue among domestic teams, but they show lower home game earnings. Therefore, we aim to predict the daily attendance at the Gwangju-KIA Champions Field of the KIA Tigers using deep learning. We collected and preprocessed daily attendance, dates, weather, and team-related variables for Gwangju-KIA Champions Field from 2018 to 2023. We propose a deep learning-based linear regression model to predict the daily attendance. We expect that the proposed deep learning model will be used as basic information to increase the club's revenue.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.20-25
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• 2021

The target-object classification method was implemented using a deep-learning-based detection model in real-time images. The object detection model was a deep-learning-based detection model that allowed extensive data collection and machine learning processes to classify similar target-objects. The recognition model was implemented by changing the processing structure of the detection model and combining developed the vision-processing module. To classify the target-objects, the identity and similarity were defined and applied to the detection model. The use of the recognition model in industry was also considered by verifying the effectiveness of the recognition model using the real-time images of an actual soccer game. The detection model and the newly constructed recognition model were compared and verified using real-time images. Furthermore, research was conducted to optimize the recognition model in a real-time environment.

• 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).