Artificial Intelligence
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative reasoning: modeling and simulation with incomplete knowledge
Automated modeling for answering prediction questions: selecting the time scale and system boundary
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
An ontology for transitions in physical dynamic systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Process Monitoring and Diagnosis: A Model-Based Approach
IEEE Expert: Intelligent Systems and Their Applications
Model-Based, Multiple-Fault Diagnosis of Dynamic, Continuous Physical Devices
IEEE Expert: Intelligent Systems and Their Applications
Estimating Monotonic Functions and Their Bounds
Estimating Monotonic Functions and Their Bounds
Semi-Quantitative System Identification
Semi-Quantitative System Identification
Refining imprecise models and their behaviors
Refining imprecise models and their behaviors
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We present a model-based monitoring method for dynamic systems that exhibit both discrete and continuous behaviors. MIMIC [Dvorak and Kuipers, 1991] uses qualitative and semiquantitative models to monitor dynamic systems even with incomplete knowledge. Recent advances have improved the quality of semi-quantitative behavior predictions, used observations to refine static envelopes around monotonic functions, and provided a semiquantitative system identification method. Using these, we reformulate and extend MIMIC to handle discontinuous changes between models. Each hypothesis being monitored is embodied as a tracker, which uses the observation stream to refine its behavioral predictions, its underlying model, and the time uncertainty of any discontinuous transitions.