• Convergence Security Journal
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• v.22 no.2
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• pp.115-120
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• 2022

This study analyzed the recent trends in the sports environment to which big data and AI technologies, which are representative technologies of the 4th Industrial Revolution, and approached them from the perspective of convergence of big data and AI technologies in the sports field. And the results are as follows. First, it is being used for player and game data analysis and team strategy establishment and operation. Second, by combining big data collected using GPS, wearable equipment, and IoT with artificial intelligence technology, scientific physical training for each player is possible through user individual motion analysis, which helps to improve performance and efficiently manage injuries. Third, with the introduction of an AI-based judgment system, it is being used for judge judgment. Fourth, it is leading the change in marketing and game broadcasting services. The technology of the 4th Industrial Revolution is bringing innovative changes to all industries, and the sports field is also in the process. The combination of big data and AI is expected to play an important role as a key technology in the rapidly changing future in a sports environment where scientific analysis and training determine victory or defeat.

• 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.

• Journal of Internet Computing and Services
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• v.18 no.2
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• pp.43-51
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• 2017

As the game industry is blooming, the generation of contents is far behind the consumption of contents. With this reason, it is necessary to afford the game contents considering level of game player's skill. In order to effectively solve this problem, Procedural Content Generation(PCG) using Artificial Intelligence(AI) is one of the plausible options. This paper proposes the procedural method to generate various monsters considering level of player's skill using genetic algorithm. One gene consists of the properties of a monster and one genome consists of genes for various monsters. A generated monster is evaluated by battle simulation with a player and then goes through selection and crossover steps. Using our proposed scheme, players adaptive monsters are generated procedurally based on genetic algorithm and the variety of monsters which are generated with different number of genome is compared.

• Journal of Korea Game Society
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• v.10 no.3
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• pp.15-24
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• 2010

Web-based MMORTS has features that distinguish it from traditional client-based games. First, Web-based MMORTS is represented by the combination of graphics and texts. Second, there is parallax agent which has a player and a base town character. This paper written for the purpose of analyzing the storytelling of web-based MMORTS, and from Innogames selected as the subjects of the study. In view of the results so far achieved, the fact, when the player logs in web-based MMORTS, the player takes the experience after some time which had taken by the AI character instead of the player logged out and User Generated Storytelling created from this process, become known. This paper has a meaning for Web-based virtual world which can juxtaposition with routine tasks and can be linked with other platforms.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.733-740
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• 2024

The purpose of this study is to activate rehabilitation exercises to prevent MCI (mild cognitive impairment) in the elderly at home. Through the convergence of AI and rehabilitation exercise, we will be able to provide integrated services of medical care and rehabilitation exercise that enable integrated health management by activating rehabilitation exercise linkage in medically vulnerable communities such as rural areas and establishing exercise data. To this end, as a convergence development plan for AI and rehabilitation exercise, interdisciplinary experts will participate to produce and distribute game and rehabilitation exercise instruction manuals by type to improve cognitive function and musculoskeletal function, and systematize rehabilitation exercise programs needed for the elderly at home. The development, operation, and education of physical fitness assessment manuals can be expanded and will be of great help in early prevention of dementia.

• Journal of Korea Game Society
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• v.15 no.5
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• pp.109-118
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• 2015

The game of Go originated from ancient China is regarded as one of the most difficult challenges in the filed of AI. Over the past few years, the top computer Go programs based on MCTS have surprisingly beaten professional players with handicap. MCTS is an approach that simulates a random sequence of legal moves until the game is ended, and replaced the traditional knowledge-based approach. We applied the UCT algorithm which is a MCTS variant to the game of Tic-Tac-Toe for finding the best first move, and compared it with the result generated by a pure MCTS. Furthermore, we introduced and compared the performances of epsilon-Greedy algorithm and UCB algorithm for solving the Multi-Armed Bandit problem to understand the UCB.

• Journal of The Korean Association of Information Education
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• v.23 no.3
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• pp.229-235
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• 2019

By combining the elements of the game or game in education, it improves the interest of the students and improves academic achievement by creating an environment where they can participate directly. We propose a curriculum that can learn the core principles of the elementary curriculum through fusion. The proposed curriculum helps students to understand the principles of the elementary curriculum by learning the artificial intelligence method in board game form. Learning methods that incorporate such elements of the game will enable learners to learn the principles of IT so that they can develop their ability to understand objects from various perspectives and enhance their thinking skills. It is expected that the elementary artificial intelligence curriculum that incorporates the proposed gamification will contribute to the development of the information science curriculum, which has been highlighted recently from the 2015 curriculum.

• Journal of Korea Multimedia Society
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• v.13 no.12
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• pp.1820-1831
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• 2010

When interacting with unknown environments, an autonomous agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. The traditional multiple sequential learning model requires predefined probability of the states' transition. This paper proposes a multiple sequential learning and prediction system with definition of autonomous states to enhance the automatic performance of existing AI algorithms. In sequence learning process, the sensed states are classified into several group by a set of proposed motivation filters to reduce the learning computation. In prediction process, the learning agent makes a decision based on the estimation of each state's cost to get a high payoff from the given environment. The proposed learning and prediction algorithms heightens the automatic planning of the autonomous agent for interacting with the dynamic unknown environment. This model was tested in a virtual library.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.261-268
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• 2020

Genetic algorithms find the optimal solution by mimicking the evolution of natural organisms. In this study, the genetic algorithm was used to enable Pac-Man's reinforcement learning, and a simulator to observe the evolutionary process was implemented. The purpose of this paper is to reinforce the learning of the Pacman AI of the simulator, and utilize genetic algorithm and artificial neural network as the method. In particular, by building a low-power artificial neural network and applying it to a genetic algorithm, it was intended to increase the possibility of implementation in a low-power embedded system.

• Journal of Korea Game Society
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• v.8 no.3
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• pp.87-95
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• 2008

This paper proposes an algorithm for efficient behaviors of boids which freely move and have no predefined position. By finding the kNN and computing the value of behavioral characteristic of a boid approximately, the proposed algorithm improves the conventional spatial partitioning one. To do this, this paper defines and uses the representative boid which has the average direction and position for a group of boids. The proposed algorithm was implemented and compared with the conventional one experimentally. The results of the experimental comparisons show that the proposed algorithm outperforms the conventional one about $-5{\sim}130%$ in terms of the ratio of the number of rendering frames per the second.