Decompositional, Model-based Learning and its Analogy to Diagnosis
A new generation of sensor rich, massively distributed autonomous system is being developed, such as smart buildings and reconfigurable factories. To achieve high performance these systems will need to accurately model themselves and their environment from sensor information. Accomplishing this on a...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.1.6171 2023-05-15T16:01:17+02:00 Decompositional, Model-based Learning and its Analogy to Diagnosis Brian C. Williams William Millar The Pennsylvania State University CiteSeerX Archives 1998 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.6171 http://mers.csail.mit.edu/papers/moriarty-aaai98.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.6171 http://mers.csail.mit.edu/papers/moriarty-aaai98.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://mers.csail.mit.edu/papers/moriarty-aaai98.pdf text 1998 ftciteseerx 2016-01-07T13:08:44Z A new generation of sensor rich, massively distributed autonomous system is being developed, such as smart buildings and reconfigurable factories. To achieve high performance these 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. To this end we have developed decompositional, model-based learning (DML). DML takes a parameterized model and sensed variables as input, decomposes it, and synthesizes a coordinated sequence of "simplest" estimation tasks. The method exploits a rich analogy between parameter estimation 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. Text DML Unknown Moriarty ENVELOPE(165.967,165.967,-73.667,-73.667) |
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English |
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A new generation of sensor rich, massively distributed autonomous system is being developed, such as smart buildings and reconfigurable factories. To achieve high performance these 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. To this end we have developed decompositional, model-based learning (DML). DML takes a parameterized model and sensed variables as input, decomposes it, and synthesizes a coordinated sequence of "simplest" estimation tasks. The method exploits a rich analogy between parameter estimation 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. |
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The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Brian C. Williams William Millar |
spellingShingle |
Brian C. Williams William Millar Decompositional, Model-based Learning and its Analogy to Diagnosis |
author_facet |
Brian C. Williams William Millar |
author_sort |
Brian C. Williams |
title |
Decompositional, Model-based Learning and its Analogy to Diagnosis |
title_short |
Decompositional, Model-based Learning and its Analogy to Diagnosis |
title_full |
Decompositional, Model-based Learning and its Analogy to Diagnosis |
title_fullStr |
Decompositional, Model-based Learning and its Analogy to Diagnosis |
title_full_unstemmed |
Decompositional, Model-based Learning and its Analogy to Diagnosis |
title_sort |
decompositional, model-based learning and its analogy to diagnosis |
publishDate |
1998 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.6171 http://mers.csail.mit.edu/papers/moriarty-aaai98.pdf |
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ENVELOPE(165.967,165.967,-73.667,-73.667) |
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Moriarty |
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Moriarty |
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DML |
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DML |
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http://mers.csail.mit.edu/papers/moriarty-aaai98.pdf |
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.6171 http://mers.csail.mit.edu/papers/moriarty-aaai98.pdf |
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Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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