• Journal of Korea Game Society
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• v.11 no.1
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• pp.15-24
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• 2011

The convergence of education and game came from efforts to compensate for the boredom of learning. As the digital technology has been developed, this new integrated field accrued to the birth of the Educational Serious Game. It has been noted on both side of industry and academic research. Despite all natural concerns, studies of the educational Serious Game tend to show the partial directivity on the learning aspects rather than the nature of the Educational Serious Game. Therefore in this study the combination aspects of fun and learning in the Educational Serious Game through various case studies is to analyse.

• Smart Media Journal
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• v.13 no.2
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• pp.32-43
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• 2024

Game agents which are behavioral agent for game playing are a crucial component of game satisfaction. However it takes a lot of time and effort to create game agents for various game levels, environments, and players. In addition, when the game environment changes such as adding contents or updating characters, new game agents need to be developed and the development difficulty gradually increases. And it is important to have a game agent that can be customized for different levels of players. This is because a game agent that can play games of various levels is more useful and can increase the satisfaction of more players than a high-level game agent. In this paper, we propose a method for learning and controlling the level of play of game agents that can be rapidly developed and fine-tuned for various game environments and changes. At this time, reinforcement learning applies a policy-based distributed reinforcement learning method IMPALA for flexible processing and fast learning of various behavioral structures. Once reinforcement learning is complete, we choose actions by sampling based on Softmax-Temperature method. From this result, we show that the game agent's play level decreases as the Temperature value increases. This shows that it is possible to easily control the play level.

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.91-97
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• 2007

Game program had been classed by 3D or on-line game etc, and engine and game programming simply, But, game programmer's kind more classified new, Artifical Intelligence game programmer's role is important. This paper makes game character study and moved by intelligence using reinforcement learning algorithm. Fought with character enemy using developed game, Confirmed whether embodied game character is facile by intelligence, As result of an experiment, we know, studied character defends excellently than randomly moved character.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.12
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• pp.1657-1664
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• 2018

This paper proposes a decision making scheme for choosing the best move at each state of game in order to implement an artificial intelligence othello game player. The proposed decision making scheme predicts the various possible states of the game when the game has progressed from the current state, evaluates the degree of possibility of winning or losing the game at the states, and searches the best move based on the evaluation. In this paper, we generate learning data by decomposing the records of professional players' real game into states, matching and accumulating winning points to the states, and using the Artificial Neural Network that learned them, we evaluated the value of each predicted state and applied the Minimax search to determine the best move. We implemented an artificial intelligence player of the Othello game by applying the proposed scheme and evaluated the performance of the game player through games with three different artificial intelligence players.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3771-3788
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• 2015

This paper studies the distributed energy efficient resource management in the Internet of Things (IoT). Wireless communication networks support the IoT without limitation of distance and location, which significantly impels its development. We study the communication channel and energy management in the wireless communication network supported IoT to improve the ability of connection, communication, share and collaboration, by using the game theory and distributed learning algorithm. First, we formulate an energy efficient neighbor collaborative game model and prove that the proposed game is an exact potential game. Second, we design a distributed energy efficient channel selection learning algorithm to obtain the global optimum in a distributed manner. We prove that the proposed algorithm will asymptotically converge to the global optimum with geometric speed. Finally, we make the simulations to verify the theoretic analysis and the performance of proposed algorithm.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.119-128
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• 2019

Minecraft game is a sandboxed game based on a high degree of users' freedom; the game encourages its users to recreate various play patterns to increase their immersion. Although recently there were many studies that use Minecraft game techniques to improve the teaching methods but still not well adapted due to being applications-based techniques. In this paper, we present a teaching model that utilizes the same concept of the Minecraft games in where learners customize the class concepts based on their needs. Moreover, Minecraft-based learning games attempt to be used for learner-led, creativity and programming instruction, to overcome these use-cases limitations, by our study we aim to include the Minecraft-based learning games in class teaching activities, theoretical and practical lessons. In this way, we intend to increase interest in programming lessons, and to increase immersion as another way of game learning. In the future, we attempt to measure various effects of the uses of Minecraft-game-based teaching in programming classes compare to the traditionally used methods.

• International Journal of Contents
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• v.16 no.2
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• pp.78-91
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• 2020

In this study, an investigation was conducted into the influencing factors for the learning motivation of players in the game, including experience, vicarious experience, the need of achievement, the need of power, and mastery motivation. Then, a discussion was conducted regarding the role played by learning motivation, learning performance, and satisfaction with continuous use. A survey was conducted with 519 players, most at the intermediate gaming level in . As demonstrated by the results of this study, experience, vicarious experience, the need of power, and the mastery of motivation have significant positive association with the players' motivation of learning the game. Learning performance and satisfaction have a positive impact on the continuity of use. Additionally, the correlation between the need of achievement and learning motivation is insignificant. Overall, the research results confirm the significance of the social-cognitive theory relative to the learning motivation. Players began to transform, satisfied with their achievements in the game, as well as gradually evolving toward self-improvement to achieve satisfaction. It offers a new explanation and crucial reference for mastering the gaming trend among the contemporary players.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.236-242
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• 2003

This paper presents a cooperation strategy between a human operator and autonomous robots for an interactive robot soccer game, The interactive robot soccer game has been developed to allow humans to join into the game dynamically and reinforce entertainment characteristics. In order to make these games more interesting, a cooperation strategy between humans and autonomous robots on a team is very important. Strategies can be pre-programmed or learned by robots themselves with learning or evolving algorithms. Since the robot soccer system is hard to model and its environment changes dynamically, it is very difficult to pre-program cooperation strategies between robot agents. Q-learning - one of the most representative reinforcement learning methods - is shown to be effective for solving problems dynamically without explicit knowledge of the system. Therefore, in our research, a Q-learning based learning method has been utilized. Prior to utilizing Q-teaming, state variables describing the game situation and actions' sets of robots have been defined. After the learning process, the human operator could play the game more easily. To evaluate the usefulness of the proposed strategy, some simulations and games have been carried out.

• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.157-164
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• 2003

To enhance the effect of learning in vocabulary learning, it is important first of all for learner to take active learning and be interested in learning, not cramming education. Due to this background, the instructor sometimes takes advantage of the game concepts in producing the courseware for vocabulary learning. But because of overwhelming overload in making the game-based courseware, it have been not made practical application to vocabulary learning. In this research, we study on the strategy for making practical application of the game concept to producing the courseware for vocabulary learning. And, based on the strategy, we design and implement the authoring tool of courseware for vocabulary learning based on crossword puzzle game. This tool enables instructor to produce the courseware based on crossword puzzle game easily and quickly and to make efficiently the courseware for vocabulary learning with level.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4799-4818
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• 2015

In this paper, we investigate the problem of achieving global optimization for distributed carrier aggregation (CA) in small cell networks, using a game theoretic solution. To cope with the local interference and the distinct cost of intra-band and inter-band CA, we propose a non-cooperation game which is proved as an exact potential game. Furthermore, we propose a spatial adaptive play learning algorithm with heterogeneous learning parameters to converge towards NE of the game. In this algorithm, heterogeneous learning parameters are introduced to accelerate the convergence speed. It is shown that with the proposed game-theoretic approach, global optimization is achieved with local information exchange. Simulation results validate the effectivity of the proposed game-theoretic CA approach.