基于RMM和贝叶斯学习的城市交通多智能体系统

Urban Traffic Multi-agent System Based on RMM and Bayesian Learning

提出一种基于递归建模方法 (RMM)的多智能体协调方法 ,使智能体在分布式环境下对其它智能体的决策建模选择合理的行动。对 RMM中的信念更新采用贝叶斯学习方法 ,使智能体可以确定其它智能体的准确模型并实时更新信息。在城市交通控制领域建立多智能体交通控制系统 ,仿真结果表明了该方法的有效性 ,对实现智能交通系统具有重要意义。

A multi agent coordination is addressed in urban traffic control, which uses the recursive modeling method(RMM) that enables an agent to select its[KG2/3]rational[KG2/3]action[KG2/3]by[KG2/3]examining[KG1/3]with[KG1/3]other agents by modeling their decision making in a distributed multi agent environment. Bayesian learning is used in conjunction with RMM for belief update. Based on this method, a multi agent traffic control system is established and the results demonstrated its effective.