• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.149.5-149
• /
• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent´s dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless ...

• Journal of Chest Surgery
• /
• v.44 no.4
• /
• pp.298-300
• /
• 2011

A 38-year-old male was admitted to our hospital due to painful swelling of his right popliteal fossa. He had been kicked in his right popliteal fossa during a soccer game about three weeks earlier. Computerized tomographic angiography of the lower extremity demonstrated a 4-cm-wide, 3-cm-long aneurysmal change of the popliteal artery. He underwent aneurysmectomy and graft interposition using a great saphenous vein graft. Pathologic findings of the resected specimen were consistent with those of a pseudoaneurysm.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.879-884
• /
• 2004

This paper describes the development of a synthetic multi-agent called KMITL Cadence 2003. KMITL Cadence 2003 is a robotic soccer simulation team consisting of eleven autonomous software agents. Each agent operates in a physical soccer simulation model called Robocup Soccer Server which provides fully distributed and real-time multi-agent system environment. All teammates have to cooperate to achieve the common goal of winning the game. The simulation models many aspects of the football field such as noise in ball movements, noisy sensors, unreliable communication channel between teammates and actuators, limited physical abilities and restricted communication. This paper addresses the algorithm to develop the soccer agents to perform basic actions which are scoring, passing ball and blocking the opponents effectively. The result of this development is satisfactory because the successful scoring attempts is increased from 11.1% to 33.3%, successful passing ball attempts is increased from 22.08% to 63.64%, and also, successful intercepting attempts is increased from 88% to 97.73%.

• Proceedings of the IEEK Conference
• /
• 2000.07b
• /
• pp.727-730
• /
• 2000

Recently, RoboCup soccer simulation has been regarded as a good benchmark problem for multiagent researches. Soccer agents have to make decision based on visual and auditory information, which are sent from the soccer server. In order to develop a strong team, we have to design decision-making process of each player agent. However, it is very difficult for us to design the decision-making processes in detail, because we don't know what actions of each player are effective for the team. In this paper, we attempt to apply co-evolutionary method, which is one type of analogies of evolution, to improve the team play. Agents have hand coded basic skills, which include dribble, shoot, pass etc. Agents already can play autonomously and independently. Individual agent skills are characterized by some parameters. By coevolving teams with these parameters, we obtained relatively interesting teams, in which players behave cooperatively in order to win the soccer game. From some experiments, we discuss what teams are evolved.

• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.5
• /
• pp.161-168
• /
• 2020

Predicting outcome of the sports enables teams to establish their strategy by analyzing variables that affect overall game flow and wins and losses. Many studies have been conducted on the prediction of the outcome of sports events through statistical techniques and machine learning techniques. Predictive performance is the most important in a game prediction model. However, statistical and machine learning models show different optimal performance depending on the characteristics of the data used for learning. In this paper, we propose a new ensemble model to predict English Premier League soccer games using statistical models and the machine learning models which showed good performance in predicting the results of the soccer games and this model is possible to select a model that performs best when predicting the data even if the data are different. The proposed ensemble model predicts game results by learning the final prediction model with the game prediction results of each single model and the actual game results. Experimental results for the proposed model show higher performance than the single models.

• Journal of Digital Convergence
• /
• v.15 no.7
• /
• pp.479-487
• /
• 2017

The purpose of this study was to identify the most influential soccer players by appling social network analysis. The subjects were the German national soccer team and the Korean national soccer team participated in the 2016 Brazil World Cup. The pass collected data provided by FIFA were analyzed by social network analysis using the Ucinet6 program and pass success rate. The results are as follows. First, the soccer player with a lot of passes had a high connection centrality in pass-through networks and high proximity. Second, the German national soccer team has appeared key players as Phillip Lahm and Kroos player, and a key player of the Korean national soccer team was Ki,S.Y. Third, the German national soccer team's quantitative indicator value of proximity center and pass success rate appeared higher than the Korean national soccer team's.

• The Journal of the Korea Contents Association
• /
• v.16 no.5
• /
• pp.205-216
• /
• 2016

This study aimed to discover the most influential player in the network of Soccer game and how individual players contribute within the team with quantitative capabilities they possess. It also examined whether or not the network of relationship between players and the coach's tactics were in harmony. The results showed high level of correlation between the network between offenders and defenders according to tactics of the coach during the first half. It can be said that each players fulfilled their role and formed a network within them to lead the game and form a virtuous network structure. On the other hand, the overall network of the team was focused on a specific player during the second half. But the coach's appropriate use of substitutes and change in tactics stabilized the network balance between the players.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.4
• /
• pp.289-293
• /
• 2002

Learning Classifier System (LCS) finds a new rule set using genetic algorithm (GA). In this paper, The Zeroth Level Classifier System (ZCS) is applied to evolving the strategy of a robot soccer simulation game (SimuroSot), which is a state varying dynamical system changed over time, as GBML (Genetic Based Machine Learning) and we show the effectiveness of the proposed scheme through the simulation of robot soccer.

• Journal of the Korean Society for Precision Engineering
• /
• v.19 no.9
• /
• pp.142-150
• /
• 2002

This study describes a method of finding an optimal path of a soccer robot by using a concentric circle method with different radii of rotation. Comparing with conventional algorithms which try to find the shortest path length, the variable concentric circle method find the shortest moving time. The radius fur the shortest moving time for a given ball location depends on the relative location between a shooting robot and a ball. Practically it is difficult to find an analytical solution due to many unknowns. Assuming a radius of rotation within a possible range, total path moving time can be calculated by adding the times needed for straight path and circular path. Among these times the shortest time is obtained. In this paper, a graphical solution is presented such that the game ground is divided into 3 regions with a minimum, medium, and maximum radius of rotation.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.24 no.6
• /
• pp.696-702
• /
• 2015

This paper suggests a new method for making a navigation path by using Bezier curve in order to improve the navigation performance used to avoid obstacles during a robot soccer game. We analyzed the advantages and disadvantages of both vector-field and limit-cycle navigation methods, which are the mostly widely used navigation methods for avoiding obstacles. To improve the disadvantages of these methods, we propose a new design technique for generating a more proper path using Bezier curve and describe its advantages. Using computer simulations and experiments, we compare the performance of vector-field navigation with that of Bezier curve navigation. The results prove that the navigation performance using Bezier curve is relatively superior to the other method.