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Automated Decomposition of Model-based Learning ProblemsA new generation of sensor rich, massively distributed autonomous systems is being developed that has the potential for unprecedented performance, such as smart buildings, reconfigurable factories, adaptive traffic systems and remote earth ecosystem monitoring. To achieve high performance these massive systems will need to accurately model themselves and their environment from sensor information. Accomplishing this on a grand scale requires automating the art of large-scale modeling. This paper presents a formalization of [\em decompositional model-based learning (DML)], a method developed by observing a modeler's expertise at decomposing large scale model estimation tasks. The method exploits a striking analogy between learning and consistency-based diagnosis. Moriarty, an implementation of DML, has been applied to thermal modeling of a smart building, demonstrating a significant improvement in learning rate.
Document ID
20020042713
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Williams, Brian C.
(RECOM Technologies, Inc. Moffett Field, CA United States)
Millar, Bill
(Caleum Research Corp. Moffett Field, CA United States)
Date Acquired
August 20, 2013
Publication Date
January 1, 1996
Subject Category
Engineering (General)
Meeting Information
Meeting: Qualitative Reasoning Workshop
Country: United States
Start Date: August 1, 1996
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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