Studies in hybrid systems: modeling, analysis, and control
Studies in hybrid systems: modeling, analysis, and control
Decompositional, model-based learning and its analogy to diagnosis
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Towars a Theory of Stochastic Hybrid Systems
HSCC '00 Proceedings of the Third International Workshop on Hybrid Systems: Computation and Control
HSCC '01 Proceedings of the 4th International Workshop on Hybrid Systems: Computation and Control
Bayesian Fault Detection and Diagnosis in Dynamic Systems
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
LICS '96 Proceedings of the 11th Annual IEEE Symposium on Logic in Computer Science
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Distributed monitoring of hybrid systems: a model-directed approach
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A model-based approach to reactive self-configuring systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Model-based programming of fault-aware systems
AI Magazine
Improving Robustness of Mobile Robots Using Model-based Reasoning
Journal of Intelligent and Robotic Systems
Robotics and Autonomous Systems
An introduction to model-based systems
AI Communications - Model-Based Systems
A causal analysis method for concurrent hybrid automata
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Combining stochastic and greedy search in hybrid estimation
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Model-based diagnosis of hybrid systems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Set-theoretic estimation of hybrid system configurations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Factory monitoring and control with mixed hardware/software, discrete/continuous models
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
A new hybrid state estimator for systems with limited mode changes
HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
Estimation of distributed hybrid systems Using particle filtering methods
HSCC'03 Proceedings of the 6th international conference on Hybrid systems: computation and control
Monitorability of stochastic dynamical systems
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Estimation and conflict detection in human controlled systems
HSCC'06 Proceedings of the 9th international conference on Hybrid Systems: computation and control
Runtime monitoring of stochastic cyber-physical systems with hybrid state
RV'11 Proceedings of the Second international conference on Runtime verification
Automatica (Journal of IFAC)
Study on image retrieval system base on multi-objective and multi-instance learning
International Journal of Wireless and Mobile Computing
Bridging control and artificial intelligence theories for diagnosis: A survey
Engineering Applications of Artificial Intelligence
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Model-based diagnosis and mode estimation capabilities excel at diagnosing systems whose symptoms are clearly distinguished from normal behavior. A strength of mode estimation, in particular, is its ability to track a system's discrete dynamics as it moves between different behavioral modes. However, often failures bury their symptoms amongst the signal noise, until their effects become catastrophic.We introduce a hybrid mode estimation system that extracts mode estimates from subtle symptoms. First, we introduce a modeling formalism, called concurrent probabilistic hybrid automata (cPHA), that merge hidden Markov models (HMM) with continuous dynamical system models. Second, we introduce hybrid estimation as a method for tracking and diagnosing cPHA, by unifying traditional continuous state observers with HMM belief update. Finally, we introduce a novel, any-time, any-space algorithm for computing approximate hybrid estimates.