A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
Diagnosis of large active systems
Artificial Intelligence
Diagnosis of discrete-event systems from uncertain temporal observations
Artificial Intelligence
Diagnosability of Discrete Event Systems with Modular Structure
Discrete Event Dynamic Systems
Model-based diagnosis in the real world: lessons learned and challenges remaining
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Incremental diagnosis of discrete-event systems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Diagnosing Process Trajectories Under Partially Known Behavior
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
A bridged diagnostic method for the monitoring of polymorphic discrete-event systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Monitoring of Active Systems With Stratified Uncertain Observations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Model-based diagnosis of discrete event systems DESs has attracted more and more attention in recent years. Online diagnosis based on actually emitted sequences of observations is very important for dynamic systems in practice. However, the observations are often uncertain. Especially, the received observation sequences may not be the actually emitted ones completely. In this paper, we use directed acyclic graphs DAGs for modeling the partial emission orders of received observations. Furthermore, combining the concept Two restricted Successive Temporal Windows proposed by Zhao and Ouyang [AI Commun. 214 2008, 249--262], we present a novel method to online update the global emitted observation sequence DAG gradually. Experimental results show that we can reason out the emitted observation sequences by this approach effectively.