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Best-first Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation

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dc.contributor.advisor Brian Williams
dc.contributor.author Timmons, Eric en_US
dc.contributor.author Williams, Brian C. en_US
dc.contributor.other Model-based Embedded and Robotic Systems en
dc.date.accessioned 2018-05-24T21:15:06Z
dc.date.available 2018-05-24T21:15:06Z
dc.date.issued 2018-05-24
dc.identifier.uri http://hdl.handle.net/1721.1/115882
dc.description.abstract With the rise of autonomous systems, there is a need for them to have high levels of robustness and safety. This robustness can be achieved through systems that are self-repairing. Underlying this is the ability to diagnose subtle failures. Likewise, online planners can generate novel responses to exceptional situations. These planners require an accurate estimate of state. Estimation methods based on hybrid discrete/continuous state models have emerged as a method of computing precise state estimates, which can be employed for either diagnosis or planning in hybrid domains. However, existing methods have difficulty scaling to systems with more than a handful of components. Discrete state estimation capabilities can scale to this level by combining best-first enumeration and conflict-directed search. Best-first methods have been developed for hybrid estimation, but the creation of conflict-directed methods has previously been elusive. While conflicts are used to learn from constraint violation, probabilistic hybrid estimation is relatively unconstrained. In this paper we present an approach to hybrid estimation that unifies best-first enumeration and conflict-directed search through the concept of "bounding" conflicts, an extension of conflicts that represent tighter bounds on the cost of regions of the search space. This paper presents a general best-first search and enumeration algorithm based on bounding conflicts (A*BC) and a hybrid estimation method based on this enumeration algorithm. Experiments show that an A*BC powered state estimator produces estimates faster than the current state of the art, particularly on large systems. en_US
dc.format.extent 27 p. en_US
dc.relation.ispartofseries MIT-CSAIL-TR-2018-016
dc.subject belief revision and update en_US
dc.subject diagnosis en_US
dc.subject heuristics en_US
dc.subject search en_US
dc.title Best-first Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation en_US
dc.date.updated 2018-05-24T21:15:06Z


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