A linear-time transformation of linear inequalities into conjunctive normal form
Information Processing Letters
Some Computational Aspects of distance-sat
Journal of Automated Reasoning
Solving Optimization Problems with DLL
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Fifty-five solvers in vancouver: the SAT 2004 competition
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
SAT-Based Planning with Minimal-#actions Plans and "soft" Goals
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
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
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Propositional satisfiability (SAT) is one of the most important and central problems in Artificial Intelligence and Computer Science. Basically, most SAT solvers are based on the well-known Davis-Logemann-Loveland (DLL) procedure. DLL is a decision procedure: given a SAT formula φ, it can decide if φ is satisfiable (and it can return a satisfying assignment μ), or not. Often, this is not suffi- cient, in that we would like μ to be also “optimal”, i.e., that has also to minimize/ maximize a given objective function. max-sat, min-one, distance-sat and their weighted versions are popular optimization problems. (In the following, φ is the input formula expressed as a set of clauses). Almost all the systems that can deal with these problems follow a classical branch&bound schema: whenever a satisfying assignment μ for φ with a cost cμ is found, the search goes on looking for another satisfying assignment with a lower (or higher, depending on the problem) cost.