Artificial Intelligence
A logical approach to efficient Max-SAT solving
Artificial Intelligence
Transforming Inconsistent Subformulas in MaxSAT Lower Bound Computation
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Detecting disjoint inconsistent subformulas for computing lower bounds for Max-SAT
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Within-problem learning for efficient lower bound computation in Max-SAT solving
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
New inference rules for Max-SAT
Journal of Artificial Intelligence Research
Resolution in Max-SAT and its relation to local consistency in weighted CSPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
MiniMaxSAT: a new weighted Max-SAT solver
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
On inconsistent clause-subsets for Max-SAT solving
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Structural relaxations by variable renaming and their compilation for solving MinCostSAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Clone: solving weighted Max-SAT in a reduced search space
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Resolution-based lower bounds in MaxSAT
Constraints
A new upper bound for Max-2-SAT: A graph-theoretic approach
Journal of Discrete Algorithms
Using learnt clauses in MAXSAT
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Efficient and accurate haplotype inference by combining parsimony and pedigree information
ANB'10 Proceedings of the 4th international conference on Algebraic and Numeric Biology
Application of logic synthesis to the understanding and cure of genetic diseases
Proceedings of the 49th Annual Design Automation Conference
Multi-agent plan recognition with partial team traces and plan libraries
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Solving disjunctive temporal problems with preferences using maximum satisfiability
AI Communications - 18th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
Improving SAT-Based weighted MaxSAT solvers
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Natural Max-SAT encoding of Min-SAT
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Artificial Intelligence
A SAT-based approach to cost-sensitive temporally expressive planning
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Planning as satisfiability with IPC simple preferences and action costs
AI Communications
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We investigate the role of cycles structures (i.e., subsets of clauses of the form $\bar{l}_{1}\vee l_{2}, \bar{l}_{1}\vee l_{3},\bar{l}_{2}\vee\bar{l}_{3}$) in the quality of the lower bound (LB) of modern MaxSAT solvers. Given a cycle structure, we have two options: (i) use the cycle structure just to detect inconsistent subformulas in the underestimation component, and (ii) replace the cycle structure with $\bar{l}_{1},l_{1}\vee\bar{l}_{2}\vee\bar{l}_{3},\bar{l}_{1}\vee l_{2}\vee l_{3}$ by applying MaxSAT resolution and, at the same time, change the behaviour of the underestimation component. We first show that it is better to apply MaxSAT resolution to cycle structures occurring in inconsistent subformulas detected using unit propagation or failed literal detection. We then propose a heuristic that guides the application of MaxSAT resolution to cycle structures during failed literal detection, and evaluate this heuristic by implementing it in MaxSatz, obtaining a new solver called MaxSatz c . Our experiments on weighted MaxSAT and Partial MaxSAT instances indicate that MaxSatz c substantially improves MaxSatz on many hard random, crafted and industrial instances.