Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Lisp and Symbolic Computation
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
On the run-time behaviour of stochastic local search algorithms for SAT
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Guided Local Search for Solving SAT and Weighted MAX-SAT Problems
Journal of Automated Reasoning
A Discrete Lagrangian-Based Global-SearchMethod for Solving Satisfiability Problems
Journal of Global Optimization
Towards Fewer Parameters for SAT Clause Weighting Algorithms
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Dynamic Constraint Weighting for Over-Constrained Problems
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
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
The theory and applications of discrete constrained optimization using lagrange multipliers
The theory and applications of discrete constrained optimization using lagrange multipliers
Local search algorithms for partial MAXSAT
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of 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
Tabu search techniques for large high-school timetabling problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Local search techniques have attracted considerable interest in the AI community since the development of GSAT for solving large propositional SAT problems. Newer SAT techniques, such as the Discrete Lagrangian Method (DLM), have further improved on GSAT and can also be applied to general constraint satisfaction and optimisation. However, little work has applied local search to MAX-SAT problems with hard and soft constraints. As many real-world problems are best represented by hard (mandatory) and soft (desirable) constraints, the development of effective local search heuristics for this domain is of significant practical importance.This paper extends previous work on dynamic constraint weighting by introducing a two-level heuristic that switches search strategy according to whether a current solution contains unsatisfied hard constraints. Using constraint weighting techniques derived from DLM to satisfy hard constraints, we apply a Tabu search to optimise the soft constraint violations. These two heuristics are further combined with a dynamic hard constraint multiplier that changes the relative importance of the hard constraints during the search. We empirically evaluate this new algorithm using a set of randomly generated 3-SAT problems of various sizes and difficulty, and in comparison with various state-of-the-art SAT techniques. The results indicate that our dynamic, two-level heuristic offers significant performance benefits over the standard SAT approaches.