A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Local Search Characteristics of Incomplete SAT Procedures
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An adaptive noise mechanism for walkSAT
Eighteenth national conference on Artificial intelligence
Clause Weighting Local Search for SAT
Journal of Automated Reasoning
Additive versus multiplicative clause weighting for SAT
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Old resolution meets modern SLS
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
The exponentiated subgradient algorithm for heuristic Boolean programming
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
The breakout method for escaping from local minima
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Evidence for invariants in local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Adding new clauses for faster local search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Diversification and determinism in local search for satisfiability
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
Weight redistribution for unweighted MAX-SAT
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Local search with edge weighting and configuration checking heuristics for minimum vertex cover
Artificial Intelligence
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In recent years, dynamic local search (DLS) clause weighting algorithms have emerged as the local search state-of-the-art for solving propositional satisfiability problems. However, most DLS algorithms require the tuning of domain dependent parameters before their performance becomes competitive. If manual parameter tuning is impractical then various mechanisms have been developed that can automatically adjust a parameter value during the search. To date, the most effective adaptive clause weighting algorithm is RSAPS. However, RSAPS is unable to convincingly outperform the best non-weighting adaptive algorithm AdaptNovelty+, even though manually tuned clause weighting algorithms can routinely outperform the Novelty+ heuristic on which AdaptNovelty+ is based. In this study we introduce R+DDFW+, an enhanced version of the DDFW clause weighting algorithm developed in 2005, that not only adapts the total amount of weight according to the degree of stagnation in the search, but also incorporates the latest resolution-based preprocessing approach used by the winner of the 2005 SAT competition (R+ AdaptNovelty+). In an empirical study we show R+DDFW+ improves on DDFW and outperforms the other leading adaptive (R+Adapt-Novelty+, R+RSAPS) and non-adaptive (R+G2WSAT) local search solvers over a range of random and structured benchmark problems.