Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
A spectrum of definitions for temporal model-based diagnosis
Artificial Intelligence
Diagnosis of large active systems
Artificial Intelligence
Model-based Diagnosis in the Real World: Lessons Learned and Challenges Remaining
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Flexible diagnosis of discrete-event systems by similarity-based reasoning techniques
Artificial Intelligence
Model-based diagnosis using structured system descriptions
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Diagnosability testing with satisfiability algorithms
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
Local Consistency and Junction Tree for Diagnosis of Discrete-Event Systems
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
A Decentralised Symbolic Diagnosis Approach
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
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This paper deals with the monitoring and diagnosis of large discrete-event systems. The problem is to determine, on-line, all faults and states that explain the flow of observations. Model-based diagnosis approaches that first compile the diagnosis information off-line suffer from space explosion, and those that operate on-line without any prior compilation have poor time performance. Our contribution is a broader spectrum of approaches that suits applications with diverse time and space requirements. Approaches on this spectrum differ in the amount of reasoning and compilation performed off-line and therefore in the way they resolve the tradeoff between the space occupied by the compiled information and the time taken to produce a diagnosis. We tackle the space and time complexity of diagnosis by encoding all approaches in a symbolic framework based on binary decision diagrams. This allows for the compact representation of the compiled diagnosis information, and for its handling across many states at once rather than for each state individually. Our experiments demonstrate the diversity and scalability of our symbolic methods spectrum, as well as its superiority over the corresponding enumerative implementations.