A theory of diagnosis from first principles
Artificial Intelligence
A survey on knowledge compilation
AI Communications
Debugging Incoherent Terminologies
Journal of Automated Reasoning
Algorithms for Computing Minimal Unsatisfiable Subsets of Constraints
Journal of Automated Reasoning
Checking Safety by Inductive Generalization of Counterexamples to Induction
FMCAD '07 Proceedings of the Formal Methods in Computer Aided Design
Property-directed incremental invariant generation
Formal Aspects of Computing
On Approaches to Explaining Infeasibility of Sets of Boolean Clauses
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Reveal: A Formal Verification Tool for Verilog Designs
LPAR '08 Proceedings of the 15th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning
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
QUICKXPLAIN: preferred explanations and relaxations for over-constrained problems
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Journal of Artificial Intelligence Research
Diagnosing and solving over-determined constraint satisfaction problems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
Identifying Necessary Reactions in Metabolic Pathways by Minimal Model Generation
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Boosting minimal unsatisfiable core extraction
Proceedings of the 2010 Conference on Formal Methods in Computer-Aided Design
On improving MUS extraction algorithms
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Faster extraction of high-level minimal unsatisfiable cores
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Managing SAT inconsistencies with HUMUS
Proceedings of the Sixth International Workshop on Variability Modeling of Software-Intensive Systems
An efficient diagnosis algorithm for inconsistent constraint sets
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
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
A scalable algorithm for minimal unsatisfiable core extraction
SAT'06 Proceedings of the 9th international conference on Theory and Applications 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 computing minimal equivalent subformulas
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Understanding, improving and parallelizing MUS finding using model rotation
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
On computing minimal correction subsets
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
On lazy and eager interactive reconfiguration
Proceedings of the Eighth International Workshop on Variability Modelling of Software-Intensive Systems
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The importance and impact of the Boolean satisfiability (SAT) problem in many practical settings is well-known. Besides SAT, a number of computational problems related with Boolean formulas find a wide range of practical applications. Concrete examples for CNF formulas include computing prime implicates (PIs), minimal models (MMs), minimal unsatisfiable subsets (MUSes), minimal equivalent subsets (MESes) and minimal correction subsets (MCSes), among several others. This paper builds on earlier work by Bradley and Manna and shows that all these computational problems can be viewed as computing a minimal set subject to a monotone predicate, i.e. the MSMP problem. Thus, if cast as instances of the MSMP problem, these computational problems can be solved with the same algorithms. More importantly, the insights provided by this result allow developing a new algorithm for the general MSMP problem, that is asymptotically optimal. Moreover, in contrast with other asymptotically optimal algorithms, the new algorithm performs competitively in practice. The paper carries out a comprehensive experimental evaluation of the new algorithm on the MUS problem, and demonstrates that it outperforms state of the art MUS extraction algorithms.