• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.978-982
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• 2009

This paper proposes a potential-based reinforcement learning, called "RLs-CBDT", which combines multiple RL agents and case-base decision theory designed for decision making in uncertain environment as an expert knowledge in RL. We empirically show that RLs-CBDT converges to an optimal policy faster than pre-existing RL algorithms through a Tetris experiment.

• Journal of the Korea Convergence Society
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• v.8 no.6
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• pp.37-44
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• 2017

Mobile devices provide various services through payment and authentication by inputting important information such as passwords on the screen with the virtual keypads. In order to infer the password inputted by the user, the attacker captures the user's touch location information. The attacker is able to infer the password by using the location information or to obtain password information by peeping with Google Glass or Shoulder Surfing Attack. As existing secure keypads place the same letters in a set order except for few keys, considering handy input, they are vulnerable to attacks from Google Glass and Shoulder Surfing Attack. Secure keypads are able to improve security by rearranging various shapes and locations. In this paper, we propose secure keypads that generates 13 different shapes and sizes of Tetris and arranges keypads to be attached one another. Since the keypad arranges different shapes and sizes like the game, Tetris, for the virtual keypad to be different, it is difficult to infer the inputted password because of changes in size even though the attacker knows the touch location information.

• Korean Architects
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• s.643
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• pp.60-65
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• 2022

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.194-199
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• 2006

본 논문에서는 불확실한 환경 상에서의 의사결정 알고리즘인 "Case-based Decision Theory" (CBDT) 알고리즘을 dynamic하게 연동되는 연속된 의사결정 문제에 대하여 강화학습의 대표적인 Q-learning의 강화기법을 응용하여 확장한 새로운 의사결정 알고리즘 "Dynamic CBDT"를 제안하고, CBDT알고리즘에 대한 Dynamic CBDT의 효율성을 테트리스 실험을 통하여 확인한다.

• The Journal of Society for e-Business Studies
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• v.25 no.2
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• pp.15-27
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• 2020

This study starts with the idea that the process of creating a Gantt Chart for schedule planning is similar to Tetris game with only a straight line. In Tetris games, the X axis is M machines and the Y axis is time. It is assumed that all types of orders can be worked without separation in all machines, but if the types of orders are different, setup cost will be incurred without delay. In this study, the game described above was named Gantris and the game environment was implemented. The AI-scheduling table through in-depth reinforcement learning compares the real-time scheduling table with the human-made game schedule. In the comparative study, the learning environment was studied in single order list learning environment and random order list learning environment. The two systems to be compared in this study are four machines (Machine)-two types of system (4M2T) and ten machines-six types of system (10M6T). As a performance indicator of the generated schedule, a weighted sum of setup cost, makespan and idle time in processing 100 orders were scheduled. As a result of the comparative study, in 4M2T system, regardless of the learning environment, the learned system generated schedule plan with better performance index than the experimenter. In the case of 10M6T system, the AI system generated a schedule of better performance indicators than the experimenter in a single learning environment, but showed a bad performance index than the experimenter in random learning environment. However, in comparing the number of job changes, the learning system showed better results than those of the 4M2T and 10M6T, showing excellent scheduling performance.

• Journal of Korea Game Society
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• v.13 no.5
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• pp.19-30
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• 2013

This Study analyzes dynamic of a puzzle game play by applying entropy law. Entropy is a concept that a quantitative measure of the amount of thermal energy not available to do work in a closed system. And amount of entropy can be measured only if we see the closed system as whole, the field. Puzzle game is also closed system. When player moves an object in game, it change a relationship among objects in play field. In , through an act of position change, player sustains a play field active. In respect of an entropy, this kind of play is considered as pursue of usability of the energy. In , player piles up objects without empty space. In respect of an entropy, this kind of play is considered as pursue of the order. Likewise, puzzle game play can be considered as simulation of a human's pursue of the order in an entropy increasing physical world. And this pursue is a driving force of puzzle game play.

• Archives of design research
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• v.19 no.1 s.63
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• pp.119-128
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• 2006

'Play' itself is enough of a motivation for anyone to do it. Still, it will be very difficult to continue to enjoy meaningless and aimless repetition of fights, mimicking, chases, discoveries or changing of sceneries by the mechanism alone. Of course, in rare occasions there are games like Tetris which can be enjoyed on its great gameplay alone for hours. But for most cases, players need goals, initiatives, and dynamism through storytelling for their experiences to be a rich one. Validity and feasibility of storytelling in games has always existed with plenty of skepticism. However, as games evolved from some small play mechanisms for spare times to a major entertainment with recognizable volume and content, a need to keep players interested and participating has made storytelling an essential ingredient. Storytelling within games has to have different meanings and shapes to existing narratives. Hence, new definition and methodology must emerge and studies has been active. If a case can be made so that a tested and tried methodology that has been successful for other media can be substituted for games, then it can bring a new direction to the ongoing studies. This study will borrow some methodology from cinema which can sometimes be seen as opposite to games and sometimes as something games want to be alike.