• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.598-601
• /
• 2016

최근 알파고로 인해 인공지능의 인기가 급부상하고 있고 '머신러닝'의 가능성과 위상을 널리 알려 컴퓨터 및 여러 분야에서 연구단계를 넘어 실용화, 상업화 될 가능성을 확인 시켜주었다. 본 연구는 전망 있는 인공지능산업에 발맞춰 비록 '알파고' 같은 고성능 완벽한 인공지능이 아니지만 랜덤 상태의 초기에서 한 최적의 해를 찾기 위한 도구로서, 유전알고리즘(genetic algorithms)을 사용하여 목표 값에는 최대한 수렴하도록 하는 학습형 AI 게임을 개발하였다. 본 연구에서 개발한 게임은 향후 각각의 다양한 개성을 가진 양산형 인공지능 게임개발에 응용되리가 사료된다.

• Journal of the Korean Society of Industry Convergence
• /
• v.27 no.2_1
• /
• pp.257-268
• /
• 2024

Soccer is type of sport that carries a high risk of injury. Injury is not only cause in the unlucky soccer carrier and also team performance as well as financial effects can be worse since soccer is a team-based game. The duration of recovery from a soccer injury typically relies on its type and severity. Therefore, we conduct this research in order to predict the probability of players injury type using machine learning technologies in this paper. Furthermore, we compare different machine learning models to find the best fit model. This paper utilizes various supervised classification machine learning models, including Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes. Moreover, based on our finding the KNN and Decision models achieved the highest accuracy rates at 70%, surpassing other models. The Random Forest model followed closely with an accuracy score of 62%. Among the evaluated models, the Naive Bayes model demonstrated the lowest accuracy at 56%. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history.

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