Artificial Intelligence
Back to the Future for Consistency-Based Trajectory Tracking
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A model-based approach to reactive self-configuring systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Robotics and Autonomous Systems
DNNF-based belief state estimation
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Set-theoretic estimation of hybrid system configurations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Materials with computational experience and style
Personal and Ubiquitous Computing
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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 significantly 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 empirical results show that BFBSE significantly increases estimator accuracy, uses less memory, and requires less computation time when enumerating a moderate number of estimates for the approximate belief state of subsystem sized models.