Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
Knowledge compilation and theory approximation
Journal of the ACM (JACM)
Decomposable negation normal form
Journal of the ACM (JACM)
Checking Safety Properties Using Induction and a SAT-Solver
FMCAD '00 Proceedings of the Third International Conference on Formal Methods in Computer-Aided Design
Planning as satisfiability: parallel plans and algorithms for plan search
Artificial Intelligence
Diagnosers and diagnosability of succinct transition systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Formal verification of diagnosability via symbolic model checking
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
A probabilistic analysis of diagnosability in discrete event systems
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Diagnosis of discrete-event systems using satisfiability algorithms
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A spectrum of symbolic on-line diagnosis approaches
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A scalable jointree algorithm for diagnosability
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Diagnosers and diagnosability of succinct transition systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-agent epistemic explanatory diagnosis via reasoning about actions
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We show how testing whether a system is diagnosable can be reduced to the satisfiability problem and how satisfiability algorithms yield a very efficient approach to testing diagnosability. Diagnosability is the question whether it is always possible to know whether a given system has exhibited a failure behavior. This is a basic question that underlies diagnosis, and it is also closely related to more general questions about the possibility to know given facts about system behavior. The work combines the twin plant construct of Jiang et al., which is the basis of diagnosability testing of systems with an enumerative representation, and SAT-based techniques to AI planning which form a very promising approach to finding paths in very large transition graphs.