The Research Of RoboCup Simulation Soccer

Multi-Agent Systems (MAS) is the main subfield of Artificial Intelligence. In complex, real-time and unpredictable systems, it requires agents to act effectively both autonomously and as part of a team, to cooperate with the teammates and defense again the opponents, to achieve the team long-term goal. The robotic soccer is such a multi-agent system, and it is a domain that fits all the above characteristics. At the same time, the robotic soccer is enjoyable and exciting. It can display research production of multi-agent system and robotics. Consider all the characteristics of the robotic soccer, the International Joint Committee of Artificial Intelligence choose robotic soccer as a standard problem of MAS. In this thesis, we use the simulation robotic soccer as test bed.First, we design an effective architecture for agent programming. Digesting former scholar's work, we correct the synchronization's bug in UvA Trilearn, and greatly improve it's debug system, which can effectively help to develop agent program. In agent's world model, we use Kalman filter to deal with noise, which greatiy improve the precision and give a reliable base for developing high-quality personal skills of agent. To personal skill of agent, we analyze the analysis way(geometry way) and experiential way based on neural networks, and give out typical algorithm. To agent' system-decision, comparing classical way based two layers (personal ability and formation), we use reinforcement learning that can let all teammates learn synchronously to seek optimal actions. Although reinforcement learning for simulated soccer has many restriction and may not bring the predictive success, it is the direction of agent's intelligence programming. To team strategy (formation layer), we propose the method to use the formation and tactics set that is dynamic and can be configured flexibly. In opponent modeling, we propose a creative method to use non-linear neural networks to recognize opponent's strategy and formation, which greatly improve agent's intelligence and match scores. Lastly, to avoid heavy and boring low-level analysis and debug work, we propose to use Matlab to simulate soccer server, which have advantage of use it's many tool boxes and many math functions such as statistical functions.