Formal methods for the validation of automotive product configuration data
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
AMUSE: a minimally-unsatisfiable subformula extractor
Proceedings of the 41st annual Design Automation Conference
Diagnosing and solving over-determined constraint satisfaction problems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Minimal Unsatisfiability: Models, Algorithms and Applications (Invited Paper)
ISMVL '10 Proceedings of the 2010 40th IEEE International Symposium on Multiple-Valued Logic
Boosting minimal unsatisfiable core extraction
Proceedings of the 2010 Conference on Formal Methods in Computer-Aided Design
Faster extraction of high-level minimal unsatisfiable cores
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Towards efficient MUS extraction
AI Communications - 18th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
On efficient computation of variable MUSes
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
Understanding, improving and parallelizing MUS finding using model rotation
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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Existing algorithms for minimal unsatisfiable subset (MUS) extraction are defined independently of any symbolic information, and in current implementations domain experts mostly do not have a chance to influence the extraction process based on their knowledge about the encoded problem. The MUStICCa tool introduces a novel graphical user interface for interactive deletion-based MUS finding, allowing the user to inspect and influence the structure of extracted MUSes. The tool is centered around an explicit visualization of the explored part of the search space, representing unsatisfiable subsets (USes) as selectable states. While inspecting the contents of any US, the user can select candidate clauses to initiate deletion attempts. The reduction steps can be enhanced by a range of state-of-the-art techniques such as clause-set refinement, model rotation, and autarky reduction. MUStICCa compactly represents the criticality information derived for the different USes in a shared data structure, which leads to significant savings in the number of solver calls when multiple MUSes are explored. For automatization, our tool includes a reduction agent mechanism into which arbitrary user-implemented deletion heuristics can be plugged.