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...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Text |
| Language: | English |
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1998
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| Subjects: | |
| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.6171 http://mers.csail.mit.edu/papers/moriarty-aaai98.pdf |
| Summary: | 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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