Research Of Player’s High-Level Skills In RoboCup Soccer Simulation

RoboCup World Cup Soccer Games and Conference(RoboCup) involves advanced research and novel technologies including artificial intelligence, robotics, sensing, communication and many other areas. Working as a simulation platform, RoboCup2D simulation game system is often employed to verify various intelligent algorithm theories and technologies by studying action skills of individual agent and collaboration strategy of the team. To emulate the constantly changing characteristic of soccer games,the RoboCup2D simulation game system offers a dynamic environment where only partial information is available to the players(agents). Under this circumstance, the choice of factors and decision-making strategies is critical to increase the winning ratio. Based on RoboCup2D platform, this thesis investigates methods of manual coding mode and machine learning for high-level actions of the agents in our team. The main work in this thesis consistes of three parts, as follows.Firstly, we make a brief introduction on the research status and existing methods involving RoboCup, with an emphasis on two major research paradigms, namely, manual coding mode and machine learning. These two paradigms are to be employed to study the decision-making of agent’s high-level action. The principle and models of sense, movement and action utilized by the soccer server are also described in this part. We also depict the structure and agent model in our team our team "GDUT-TiJi"Secondly, we focus on the study of shooting skill. After an analysis of our team’s code and game videos, we find the shortcomings of shooting skill and reasons resulting in shooting failures. We then select the manual coding way and use the grey comprehensive evaluation criterion to improve the shooting strategy according to characteristic of various comprehensive evaluation methods and RoboCup2D simulation game system. Experiment results suggest that this method improves the success rate of shooting.Finally, we investigate the passing skill. A comparison between the manual coding and machine learning leads to the conclusion that the latter is more suitable for decision-making process at the moment of passing the ball. Among various machine learning strategies, we choose the DFL-based autonomous learning as the main algorithm. After verifying the feasibility and validity of this method with an example, we finally apply this method in our team with an opponent team adopting a strong defensive strategy. Statistics shows that the rate of passing success is greatly increased by this method.