Accurate belief state update for probabilistic constraint automata
Name
61719746-MIT.pdf
Description
Full printable version
Size
11.71 MB
Format
Adobe PDF
Checksum (MD5)
0c93017ccb5612caf00b3e93a6512fc0
Author(s)
Martin, Oliver B., 1979-
Advisor(s)
Brian C. Williams.
Date Issued
2005
Publisher
Massachusetts Institute of Technology
Abstract
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 target state can significantly underestimate the true state probability and result in misdiagnosis. This thesis introduces two innovative belief state approximation techniques, called Best-First Belief State Enumeration (BFBSE) and Best-First Belief State Update (BFBSU), that address this limitation by computing estimate probabilities directly from the HMM belief state update equations. Theoretical and empirical results show that I3FBSE and BFBSU significantly increases estimator accuracy, uses less memory, and have no increase in computation time when enumerating a moderate number of estimates for the approximate belief state of subsystem sized models.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005.
Includes bibliographical references (p. 91-93).
Subjects
Aeronautics and Astronautics.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
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