Artificial Intelligence
Artificial Intelligence
Theories of causal ordering: reply to de Kleer and Brown
Artificial Intelligence
Characterizing diagnoses and systems
Artificial Intelligence
Decompositional modeling through caricatural reasoning
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Directed reduction algorithms and decomposable graphs
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Operations for learning with graphical models
Journal of Artificial Intelligence Research
Diagnosis with behavioral modes
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Reasoning about nonlinear system identification
Artificial Intelligence
Mode Estimation of Probabilistic Hybrid Systems
HSCC '02 Proceedings of the 5th International Workshop on Hybrid Systems: Computation and Control
Conflict-directed A* and its role in model-based embedded systems
Discrete Applied Mathematics
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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, modelbased 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.