Diagnosis as approximate belief state enumeration for probabilistic concurrent constraint automata

As autonomous spacecraft and other robotic systems grow increasingly complex, there is a pressing need for capabilities that more accurately monitor and diagnose system state while maintaining reactivity. Mode estimation addresses this problem by reasoning over declarative models of the physical plant, represented as a factored variant of Hidden Markov Models (HMMs), called Probabilistic Concurrent Constraint Automata (PCCA). Previous mode estimation approaches track a set of most likely PCCA state trajectories, enumerating them in order of trajectory probability. Although Best-First Trajectory Enumeration (BFTE) is efficient, ignoring the additional trajectories that lead to the same state can considerably underestimate the true state probability and result in misdiagnosis. This paper introduces an innovative belief approximation technique, called Best-First Belief State Enumeration (BFBSE), that addresses this limitation by computing estimate probabilities directly from the HMM belief state update equations. Theoretical and preliminary empirical results show that BFBSE considerably increases estimator accuracy, requires less memory, and has no increase in computation time when enumerating a moderate number of estimates for the approximate belief state of subsystem sized models.