ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Algorithms for selective enumeration of prime implicants
Artificial Intelligence
The scaling window of the 2-SAT transition
Random Structures & Algorithms
Frozen development in graph coloring
Theoretical Computer Science - Phase transitions in combinatorial problems
Introduction to Algorithms
A two-phase backbone-based search heuristic for partial MAX-SAT: an initial investigation
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
A survey on knowledge compilation
AI Communications
Solving the minimum-cost satisfiability problem using SAT based branch-and-bound search
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Probabilistically Estimating Backbones and Variable Bias: Experimental Overview
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
A New Empirical Study of Weak Backdoors
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Backbones and backdoors in satisfiability
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Propositional independence: formula-variable independence and forgetting
Journal of Artificial Intelligence Research
Backbone guided local search for maximum satisfiability
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A backbone-search heuristic for efficient solving of hard 3-SAT formulae
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Backbones in optimization and approximation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
The backbone of the travelling salesperson
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A novel local search algorithm for the traveling salesman problem that exploits backbones
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Feature models, grammars, and propositional formulas
SPLC'05 Proceedings of the 9th international conference on Software Product Lines
Post-silicon fault localisation using maximum satisfiability and backbones
Proceedings of the International Conference on Formal Methods in Computer-Aided Design
Reasoning over biological networks using maximum satisfiability
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Change propagation due to uncertainty change
FASE'13 Proceedings of the 16th international conference on Fundamental Approaches to Software Engineering
Incrementally synthesizing controllers from scenario-based product line specifications
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
Coverage-based trace signal selection for fault localisation in post-silicon validation
HVC'12 Proceedings of the 8th international conference on Hardware and Software: verification and testing
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Backbones of propositional theories are literals that are true in every model. Backbones have been used for characterizing the hardness of decision and optimization problems. Moreover, backbones find other applications. For example, backbones are often identified during product configuration. Backbones can also improve the efficiency of solving computational problems related with propositional theories. These include model enumeration, minimal model computation and prime implicant computation. This paper investigates algorithms for computing backbones of propositional theories, emphasizing the integration of these algorithms with modern SAT solvers. Experimental results, obtained on representative problem instances, indicate that the proposed algorithms are effective in practice and can be used for computing the backbones of large propositional theories. In addition, the experimental results indicate that propositional theories can have large backbones, often representing a significant percentage of the total number of variables.