A Monte Carlo approach for approximate belief state estimation of dynamic system

Given a system model and a set of observations, model-based monitoring and diagnosis of discrete dynamic system is often cast as the task of determining the likely belief state of components. This problem is tough, because the complexity is exponential to both the number of components and time steps. In this paper, an innovative approximate estimation algorithm, coined MCBSE (Monte Carlo-based Belief State Enumeration), is presented. MCBSE adopts Monte Carlo techniques to efficiently maintain a partial belief state. Moreover, the strategy `first update next allocate´ uses observation at time t+1 to calculate the real transition probability, and then distribute the particles at time t. It significantly improves the accuracy of estimator and avoids losing solutions. Empirical results show that MCBSE will outperform BFTE (Best-First Trajectory Enumeration) apparently.