Multi-agent Collaboration Strategies And Applications In Robocup

Multi-agent cooperation is an important research focus of multi-agent system (MAS). In complex, dynamic and uncertain multi-agent environment, this dissertation studies these problems, such as strategy optimization of single agent, behavior coordination and action planning, to satisfy the requirements of local collaboration and communication limitation in the process of multi-agent cooperation. Then multi-agent cooperation strategies are conducted to be applicable in different cases and examined in RoboCup soccer simulation system.Firstly, in order to implement behavior selection optimization of the agent and local collaboration of multiple agents, a multi-agent cooperation strategy based on behavior common optimization is proposed. Each agent uses modular fuzzy Q-learning to speculate the behaviors of other agents. Considering their behavior restrictions, individual behavior decision-making is optimized. Then the behavior conflicts among agents are solved by the coordination method sharing joint-intentions to obtain the optimized behavior strategy.Secondly, a layered planning cooperation strategy based on multi-agent behavior graph is presented in the case of communication limited. According to the local environment state information that agents observe, the behavior process of agents is planned using behavior graph in advance. Then combining with the prior knowledge of behavior selection obtained by modular fuzzy Q-learning, initial activity planning is gradually adjusted from lower layer to higher one, so that consistent action sequence of each agent is acquired, which ensures the agent to make action decision fleetly against current environment to cooperate with others neatly.These proposed cooperation strategies above have been applied into CSU_YunLu simulation team. The feasibility is verified in actual antagonism training and competition.