Using crude probability estimates to guide diagnosis
Artificial Intelligence
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
Characterizing diagnoses and systems
Artificial Intelligence
A spectrum of definitions for temporal model-based diagnosis
Artificial Intelligence
Remote Agent: to boldly go where no AI system has gone before
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Discrete Event Dynamic Systems
Faster SAT and smaller BDDs via common function structure
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Model-based Diagnosis in the Real World: Lessons Learned and Challenges Remaining
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on 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
Diagnosis as approximate belief state enumeration for probabilistic concurrent constraint automata
AAAI'05 Proceedings of the 20th national 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 diagnosis through OBDD compilation: a complexity analysis
Reasoning, Action and Interaction in AI Theories and Systems
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In this paper we present a model-based approach to the on-line diagnosis of dynamic systems. We model the system to be diagnosed as a discrete, synchronous transition system and capture temporal phenomena such as the change of the system inputs, the evolution of the system internal status and the evolution of the health conditions of the system components. The on-line diagnostic task consists of three subtasks: estimating the potentially highly ambiguous belief state (i.e. the set of possible system states), detecting significant changes in the belief state (in particular, changes in the set of preferred diagnoses) and presenting the preferred diagnoses to the user. We present a backtrack-free algorithm that keeps track of the complete belief state even when such a set is very large; we then introduce efficient algorithms that perform the detection of changes in the set of preferred diagnoses and the presentation of preferred diagnoses. The selection of preferred diagnoses is based on the adoption of ranks for representing the probabilities of occurrence of faults. In order to achieve completeness and efficiency, we exploit symbolic techniques (in particular, Ordered Binary Decision Diagrams) to encode and manipulate the system model and the belief state. The approach is tested on two real-world models, taken from the automotive and aerospace domains.