A machine program for theorem-proving
Communications of the ACM
Formal methods for the validation of automotive product configuration data
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Computing Horn Strong Backdoor Sets Thanks to Local Search
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
A New Empirical Study of Weak Backdoors
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Backdoors in the Context of Learning
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
The backdoor key: a path to understanding problem hardness
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Backbones and backdoors in satisfiability
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Backdoors to typical case complexity
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Tradeoffs in the complexity of backdoor detection
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Computation of renameable Horn backdoors
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
The Multivariate Algorithmic Revolution and Beyond
| Hi-index | 0.00 |
Although propositional satisfiability (SAT) is NP-complete, state-of-the-art SAT solvers are able to solve large, practical instances. The concept of backdoors has been introduced to capture structural properties of instances. A backdoor is a set of variables that, if assigned correctly, leads to a polynomial-time solvable sub-problem. In this paper, we address the problem of finding all small backdoors, which is essential for studying value and variable ordering mistakes. We discuss our definition of sub-solvers and propose algorithms for finding backdoors. We experimentally compare our proposed algorithms to previous algorithms on structured and real-world instances. Our proposed algorithms improve over previous algorithms for finding backdoors in two ways. First, our algorithms often find smaller backdoors. Second, our algorithms often find a much larger number of backdoors.