Using crude probability estimates to guide diagnosis
Artificial Intelligence
The computational complexity of abduction
Artificial Intelligence - Special issue on knowledge representation
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
Characterizing diagnoses and systems
Artificial Intelligence
Improving the Variable Ordering of OBDDs Is NP-Complete
IEEE Transactions on Computers
A spectrum of definitions for temporal model-based diagnosis
Artificial Intelligence
Algorithms and Data Structures in VLSI Design
Algorithms and Data Structures in VLSI Design
Faster SAT and smaller BDDs via common function structure
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Process algebras for systems diagnosis
Artificial Intelligence
Modeling Process Diagnostic Knowledge Through Causal Networks
AI*IA '95 Proceedings of the 4th Congress of the Italian Association for Artificial Intelligence on Topics in Artificial Intelligence
FORCE: a fast and easy-to-implement variable-ordering heuristic
Proceedings of the 13th ACM Great Lakes symposium on VLSI
Model-based diagnosis using structured system descriptions
Journal of Artificial Intelligence Research
Formal verification of diagnosability via symbolic model checking
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Planning in nondeterministic domains under partial observability via symbolic model checking
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
On the role of modeling causal independence for system model compilation with OBDDs
AI Communications - Model-Based Systems
Computation of Minimal Sensor Sets for Conditional Testability Requirements
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Parametric abstraction of behavioral modes for model-based diagnosis
AI Communications
Computing minimal diagnosis with binary decision diagrams algorithm
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
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Since it is known that Model-Based Diagnosis may suffer from a potentially exponential size of the search space, a number of techniques have been proposed for alleviating the problem. Among them, some forms of compilation of the domain model have been investigated. In the present paper we address the problem of evaluating the complexity of diagnostic problem solving when Ordered Binary Decision Diagrams are adopted for representing the normal and faulty behavior of the system to be diagnosed and the solution space. In particular we analyze the case of the diagnosis of static models that exhibit a directionality from inputs to outputs (an important example of this type of models is the class of combinatorial digital circuits). We show that the problem of determining the set of all diagnoses and of determining the minimum cardinality diagnoses can be solved in time and space polynomial with respect to the size of the OBDD encoding the domain model. These results hold regardless of the degree of system observability including whether observations are precise or uncertain. We then analyze the complexity of refining the set of diagnoses by making additional observations and by using a test vector for troubleshooting the system. In particular we show that in the latter case we lose the formal guarantee that the diagnosis can be performed in polynomial time with respect to the size of the compiled domain model.