Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
The basic algorithm for pseudo-Boolean programming revisited
Selected papers on First international colloquium on pseudo-boolean optimization and related topics
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
A machine program for theorem-proving
Communications of the ACM
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Nonserial Dynamic Programming
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Counting Models Using Connected Components
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Using weighted MAX-SAT engines to solve MPE
Eighteenth national conference on Artificial intelligence
Algorithms and Complexity Results for #SAT and Bayesian Inference
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Arc consistency for soft constraints
Artificial Intelligence
A logical approach to efficient Max-SAT solving
Artificial Intelligence
Semiring induced valuation algebras: Exact and approximate local computation algorithms
Artificial Intelligence
New inference rules for Max-SAT
Journal of Artificial Intelligence Research
MINIMAXSAT: an efficient weighted max-SAT solver
Journal of Artificial Intelligence Research
The good old Davis-Putnam procedure helps counting models
Journal of Artificial Intelligence Research
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
MaxSolver: An efficient exact algorithm for (weighted) maximum satisfiability
Artificial Intelligence
On solving the partial MAX-SAT problem
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
The generalized distributive law
IEEE Transactions on Information Theory
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In this paper we introduce an extension of propositional logic that allows clauses to be weighted with values from a generic semiring. The main interest of this extension is that different instantiations of the semiring model different interesting computational problems such as finding a model, counting the number of models, finding the best model with respect to an objective function, finding the best model with respect to several independent objective functions, or finding the set of pareto-optimal models with respect to several objective functions. Then we show that this framework unifies several solving techniques and, even more importantly, rephrases them from an algorithmic language to a logical language. As a result, several solving techniques can be trivially and elegantly transferred from one computational problem to another. As an example, we extend the basic DPLL algorithm to our framework producing an algorithm that we call SDPLL. Then we enhance the basic SDPLL in order to incorporate the three features that are common in all modern SAT solvers: backjumping, learning and restarts. As a result, we obtain an extremely simple algorithm that captures, unifies and extends in a well-defined logical language several techniques that are valid for arbitrary semirings.