Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
Modeling digital circuits for troubleshooting
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
The PEPA workbench: a tool to support a process algebra-based approach to performance modelling
Proceedings of the 7th international conference on Computer performance evaluation : modelling techniques and tools: modelling techniques and tools
A compositional approach to performance modelling
A compositional approach to performance modelling
A spectrum of definitions for temporal model-based diagnosis
Artificial Intelligence
Diagnosis of large active systems
Artificial Intelligence
A relational model of data for large shared data banks
Communications of the ACM
Coordinated Decentralized Protocols for Failure Diagnosisof Discrete Event Systems
Discrete Event Dynamic Systems
Diagnosis of quantized systems based on a timed discrete-eventmodel
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Diagnosis of a class of distributed discrete-event systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Temporal Bayesian Network of Events for Diagnosis and Prediction in Dynamic Domains
Applied Intelligence
Flexible diagnosis of discrete-event systems by similarity-based reasoning techniques
Artificial Intelligence
A diagnostic environment for automaton networks
Software—Practice & Experience
Partial Order Techniques for Distributed Discrete Event Systems: Why You Cannot Avoid Using Them
Discrete Event Dynamic Systems
Incremental processing of temporal observations in Model-Based Reasoning
AI Communications - Model-Based Systems
Model-Based Diagnosis of Discrete Event Systems with an Incomplete System Model
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Observation-Subsumption Checking in Similarity-Based Diagnosis of Discrete-Event Systems
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Dependable Monitoring of Discrete-Event Systems with Uncertain Temporal Observations
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Flexible diagnosis of discrete-event systems by similarity-based reasoning techniques
Artificial Intelligence
A theory of meta-diagnosis: reasoning about diagnostic systems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Reasoning on partially-ordered observations in online diagnosis of DESs
AI Communications
Hi-index | 0.00 |
Observations play a major role in diagnosis. The nature of an observation varies according to the class of the considered system. In static systems, an observation is the value of a variable at a single time point. In dynamic continuous systems, such a value is observed over a time interval. In discrete-event systems, an observation consists of a sequence of temporally ordered events. In any case, what is observed is assumed not to be ambiguous. This certainty principle, whilst being a useful simplification for a variety of contexts, may become inappropriate for a wide range of real systems, where the communication between the system and the observer is either bound to generate spurious messages, to randomly lose messages, or to lose temporal constraints among them. Consequently, the observation may be underconstrained. To cope with this uncertainty, a number of principles affecting both the observations and the modeled behavior of a system are introduced, that are independent of any specific processing technique. Furthermore, the notion of an uncertain temporal observation for discrete-event systems is introduced and accommodated within a graph whose nodes are labeled by uncertain messages, while edges define a partial temporal ordering among messages. This way, an uncertain observation implicitly defines a finite set of observations in the traditional sense. Thus, solving an uncertain diagnostic problem amounts to solving at one time several traditional diagnostic problems. The notion of an uncertain observation is further generalized to that of a complex observation. Both notions can be exploited by any diagnostic approach pertinent to discrete-event systems. Complex observations are contextualized in the framework of diagnosis of active systems and substantiated by a sample application in the domain of power transmission networks.