• Journal of Korea Game Society
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• v.16 no.6
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• pp.163-172
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• 2016

This study analyzes the attitudes of users who use the automatic-play function of mobile-games. It also observes changes in different emotions to enjoyment. For the analysis, the Q methodology appropriate for the subjectivity study was used, and it classified two types of attitudes based on the analysis. After the classification, it defined the characteristic of each type based on Lazzaro's theory on classification of fun. Types with high rate of convenience feature dependency utilize automatic-play feature to play games for collecting and growth, and they prefer managing the game outdoors. Another type enjoyed the experience of problem solving through games. They want the process to be a good skill to possess in game-play. There was also a tendency to perceive oneself as the game's character. Both types were responded positively to using automatic-play to play mobile-games. This is expected to be used as important data for game production, leading to usability evaluation for analyzing the emotional sensitivity of mobile game users.

• Journal of Korea Game Society
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• v.16 no.3
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• pp.27-34
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• 2016

New game systems have emerged with the rapid growth of Korea smartphone game industry. Especially auto battle system introduced in RPG which is the most popular genre, which has brought a significant change in the understanding of the computer games. To understand this change, it is necessary to look at what circumstances did emerge auto battle system. What is important here is the smart phone as the technical apparatus which defines the presence of a smart phone game, and the use pattern of the smart phone. the technical device and Its use pattern are affecting the activity of the user how to play the game, game developers are to create the game system according to this play style. By examining the appearance and growth of this auto battle system, we can confirm that the micro changes in the game system, reflect changes in the present form of a computer game. This fact will widen the understanding of the history of computer games.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.1-6
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• 2021

Most of the match-3 puzzle games supports automatic play using the MCTS algorithm. However, implementing reinforcement learning agents is not an easy job because it requires both the knowledge of machine learning and the way of complex interactions within the development environment. This study proposes a method in which we can easily design reinforcement learning agents and implement game play agents by applying PPO(Proximal Policy Optimization) algorithms. And we could identify the performance was increased about 44% than the conventional method. The tools we used are the Unity 3D game engine and Unity ML SDK. The experimental result shows that agents became to learn game rules and make better strategic decisions as experiments go on. On average, the puzzle gameplay agents implemented in this study played puzzle games better than normal people. It is expected that the designed agent could be used to speed up the game level design process.