Algorithms for Computing Minimal Unsatisfiable Subsets of Constraints

By Mark H. Liffiton, Karem A. Sakallah. Journal of automated reasoning, 2007

392 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/815b2ca4f366cba71778d2bbe7556adbd96ec841

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tomas Lozano-Perez
2. Leslie Pack Kaelbling
3. Caelan Reed Garrett
4. Tom Silver
5. Rohan Chitnis
6. Beomjoon Kim
7. Rachel Holladay
8. Josh Tenenbaum

In Computational Theory and Mathematics

1. Tomas Lozano-Perez
2. Leslie Pack Kaelbling
3. Rohan Chitnis
4. Beomjoon Kim
5. Tom Silver

In Computer Networks and Communications

1. Tomas Lozano-Perez
2. Leslie Pack Kaelbling
3. Caelan Reed Garrett
4. Andrea Lockerd Thomaz
5. Hado van Hasselt

Citations in the corpus

• Integrated Task and Motion Planning (2020, methodology): “There are methods from the discrete sat literature that, for unsatisfiable propositional formulas, identify unsatisfiable cores (80), small subsets of constraint that cause unsatisfiability.”
• Iterative and core-guided MaxSAT solving: A survey and assessment (2013, neutral): “Concrete examples include the following domains: Routing problems [127]; different problems of BioInformatics, such as Protein Alignment [120], Haplotyping with Pedigrees [56], Reasoning over Biological Networks[58]; Hardware Debugging, both on Design Debugging [111], as well as on Circuit Debugging”