Urban traffic multi-agent system based on RMM and Bayesian learning

Addresses multi-agent coordination in urban traffic control to coordinate the signals of adjacent intersections for minimizing the waiting car queue in the urban traffic network. For the purpose of this case study, we adopt a multi-agent coordination, which uses the recursive modeling method (RMM) that enables an agent to select his rational action by examining with other agents by modeling their decision making in a distributed multi-agent environment. Bayesian learning is used in conjunction with RMM for belief update. As a result, an agent can determine which models of the other agents are correct, and keep his knowledge up to date. We describe how decision making using RMM and Bayesian learning is applied to the urban traffic control domain to settle a multi-agent traffic control system and show experimental results