Advanced Search
DSpace@MIT

Accurate belief state update for probabilistic constraint automata

Research and Teaching Output of the MIT Community

Show simple item record

dc.contributor.advisor Brian C. Williams. en_US
dc.contributor.author Martin, Oliver B., 1979- en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. en_US
dc.date.accessioned 2006-03-29T18:45:14Z
dc.date.available 2006-03-29T18:45:14Z
dc.date.copyright 2005 en_US
dc.date.issued 2005 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/32446
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005. en_US
dc.description Includes bibliographical references (p. 91-93). en_US
dc.description.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. en_US
dc.description.statementofresponsibility by Oliver Borelli Martin. en_US
dc.format.extent 93 p. en_US
dc.format.extent 5062958 bytes
dc.format.extent 5067571 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Aeronautics and Astronautics. en_US
dc.title Accurate belief state update for probabilistic constraint automata en_US
dc.type Thesis en_US
dc.description.degree S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. en_US
dc.identifier.oclc 61719746 en_US


Files in this item

Name Size Format Description
61719746.pdf 11.71Mb PDF Preview, non-printable (open to all)
61719746-MIT.pdf 11.71Mb PDF Full printable version (MIT only)

This item appears in the following Collection(s)

Show simple item record

MIT-Mirage