Diagnosis as Approximate Belief State Enumeration for Probabilistic Concurrent Constraint Automata

Abstract

As autonomous spacecraft and other robotic systems grow increasingly complex, there is a pressing need for capabili-ties that more accurately monitor and diagnose system state while maintaining reactivity. Mode estimation addresses this problem by reasoning over declarative models of the physi-cal 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, enumerat-ing 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 sig-nificantly underestimate the true state probability and result in misdiagnosis. This paper introduces an innovative belief approximation technique, called Best-First Belief State Enu-meration (BFBSE), that addresses this limitation by comput-ing estimate probabilities directly from the HMM belief state update equations. Theoretical and empirical results show that BFBSE significantly increases estimator accuracy, uses less memory, and requires less computation time when enumerat-ing a moderate number of estimates for the approximate be-lief state of subsystem sized models.