An Improved Bayesian Learning Method for Multi-agent System

Abstract

A multi-agent coordinate ion is addressed in urban traffic control, which uses the recursive modeling method (RMM) that enables an agent to select its rational act ion 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. Based on this method, a multi-agent traffic control system is established and the results rated its effective.