A qualitative physics based on confluences
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Readings in qualitative reasoning about physical systems
Readings in qualitative reasoning about physical systems
Estimation theory for nonlinear models and set membership uncertainty
Automatica (Journal of IFAC)
Readings in model-based diagnosis
Readings in model-based diagnosis
Constraint reasoning based on interval arithmetic: the tolerance propagation approach
Artificial Intelligence - Special volume on constraint-based reasoning
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative reasoning: modeling and simulation with incomplete knowledge
Mode Estimation of Probabilistic Hybrid Systems
HSCC '02 Proceedings of the 5th International Workshop on Hybrid Systems: Computation and Control
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
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
Back to the Future for Consistency-Based Trajectory Tracking
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Combining stochastic and greedy search in hybrid estimation
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Diagnosis as approximate belief state enumeration for probabilistic concurrent constraint automata
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Efficient failure detection on mobile robots using particle filters with Gaussian process proposals
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Variable resolution particle filter
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Modeling time in hybrid systems: how fast is "instantaneous"?
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A model-based approach to reactive self-configuring systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Fast context switching in real-time propositional reasoning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Monitoring a complex physical system using a hybrid dynamic bayes net
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
An improvement to the interacting multiple model (IMM) algorithm
IEEE Transactions on Signal Processing
Hybrid estimation of complex systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Model-Based Diagnosis of Hybrid Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Technical Communique: Interval constraint propagation with application to bounded-error estimation
Automatica (Journal of IFAC)
Brief On combining statistical and set-theoretic estimation
Automatica (Journal of IFAC)
Bridging control and artificial intelligence theories for diagnosis: A survey
Engineering Applications of Artificial Intelligence
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Hybrid systems serve as a powerful modeling paradigm for representing complex continuous controlled systems that exhibit discrete switches in their dynamics. The system and the models of the system are nondeterministic due to operation in uncertain environment. Bayesian belief update approaches to stochastic hybrid system state estimation face a blow up in the number of state estimates. Therefore, most popular techniques try to maintain an approximation of the true belief state by either sampling or maintaining a limited number of trajectories. These limitations can be avoided by using bounded intervals to represent the state uncertainty. This alternative leads to splitting the continuous state space into a finite set of possibly overlapping geometrical regions that together with the system modes form configurations of the hybrid system. As a consequence, the true system state can be captured by a finite number of hybrid configurations. A set of dedicated algorithms that can efficiently compute these configurations is detailed. Results are presented on two systems of the hybrid system literature.