MonitoringWeb Service Networks in a Model-based Approach
ECOWS '05 Proceedings of the Third European Conference on Web Services
Formal verification of diagnosability via symbolic model checking
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Model-Based Diagnosability Analysis for Web Services
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Another Point of View on Diagnosability
Proceedings of the 2008 conference on STAIRS 2008: Proceedings of the Fourth Starting AI Researchers' Symposium
A probabilistic analysis of diagnosability in discrete event systems
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Distributed Repair of Nondiagnosability
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
A scalable jointree algorithm for diagnosability
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Diagnosability verification with Petri net unfoldings
International Journal of Knowledge-based and Intelligent Engineering Systems
Diagnosability Analysis of Discrete Event Systems with Autonomous Components
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
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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.