A Model-Based Diagnosis System for Identifying Faulty Components in Digital Circuits
Applied Intelligence
Finding all minimal unsatisfiable subsets
Proceedings of the 5th ACM SIGPLAN international conference on Principles and practice of declaritive programming
Extracting MUCs from Constraint Networks
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
MUST: provide a finer-grained explanation of unsatisfiability
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Improved algorithms for deriving all minimal conflict sets in model-based diagnosis
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Discovery of minimal unsatisfiable subsets of constraints using hitting set dualization
PADL'05 Proceedings of the 7th international conference on Practical Aspects of Declarative Languages
Towards efficient MUS extraction
AI Communications - 18th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
Quantified maximum satisfiability: a core-guided approach
SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
Preserving partial solutions while relaxing constraint networks
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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To discriminate among all possible diagnoses using Hou's theory of measurement in diagnosis from first principles, one has to derive all minimal conflict sets from a known conflict set. However, the result derived from Hou's method depends on the order of node generation in CS-trees. We develop a derivation method with mark set to overcome this drawback of Hou's method. We also show that our method is more efficient in the sense that no redundant tests have to be done. An enhancement to our method with the aid of extra information is presented. Finally, a discussion on top-down and bottom-up derivations is given