Abstract
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 c μ is found, the search goes on looking for another satisfying assignment with a lower (or higher, depending on the problem) cost.
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Giunchiglia, E., Maratea, M. (2006). optsat: A Tool for Solving SAT Related Optimization Problems. In: Fisher, M., van der Hoek, W., Konev, B., Lisitsa, A. (eds) Logics in Artificial Intelligence. JELIA 2006. Lecture Notes in Computer Science(), vol 4160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11853886_43
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DOI: https://doi.org/10.1007/11853886_43
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