Name
Abstract | ||
|---|---|---|
Diagnosability of systems is an essential property that determines how accurate any diagnostic reasoning can be on a system given any sequence of observations. Generally, in the literature of dynamic event-driven systems, diagnosability analysis is performed by algorithms that consider a system as a whole and their response is either a positive answer or a counter example. In this paper, we present an original framework for diagnosability checking. The diagnosability problem is solved in a distributed way in order to take into account the distributed nature of realistic problems. As opposed to all other approaches, our algorithm also provides an exhaustive and synthetic view of the reasons why the system is not diagnosable. Finally, the presented algorithm is scalable in practice: it provides an approximate and useful solution if the computational resources are not sufficient. |
Year | Venue | Keywords |
|---|---|---|
2007 | IJCAI | scalable diagnosability checking,positive answer,counter example,computational resource,original framework,diagnosability checking,essential property,dynamic event-driven system,diagnostic reasoning,diagnosability problem,diagnosability analysis,artificial intelligence,counter examples,algorithms |
Field | DocType | Citations |
Computer science,Theoretical computer science,Counterexample,Diagnostic reasoning,Scalability | Conference | 11 |
PageRank | References | Authors |
0.90 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anika Schumann | 1 | 103 | 13.12 |
| Yannick Pencolé | 2 | 151 | 12.01 |