Artificial Intelligence
A theory of diagnosis from first principles
Artificial Intelligence
The computational complexity of abduction
Artificial Intelligence - Special issue on knowledge representation
The complexity of logic-based abduction
Journal of the ACM (JACM)
ACM Computing Surveys (CSUR)
System Test and Diagnosis
Why is Combinational ATPG Efficiently Solvable for Practical VLSI Circuits?
Journal of Electronic Testing: Theory and Applications
Multiple Fault Diagnosis Using n-Detection Tests
ICCD '03 Proceedings of the 21st International Conference on Computer Design
Polynomially Complete Fault Detection Problems
IEEE Transactions on Computers
IEEE Design & Test
An Empirical Analysis of the Complexity of Model-Based Diagnosis
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Computing observation vectors for max-fault min-cardinality diagnoses
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Counting complexity of propositional abduction
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Automatic test pattern generation
SFM'06 Proceedings of the 6th international conference on Formal Methods for the Design of Computer, Communication, and Software Systems
Analysis and methodology for multiple-fault diagnosis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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This article formalises the dual problem to model-based diagnosis (MBD), i.e., generating tests to isolate multiple simultaneous faults. Using a standard propositional MBD framework, we first define a test of minimal size that can isolate multiple simultaneous faults of an arbitrary nature. Second, we prove complexity results for multiplefault tests of minimal size in propositional system models, showing such problems have complexity similar to those of MBD problems, i.e., complexity at the second level of the polynomial hierarchy.