A New Clause Learning Scheme for Efficient Unsatisfiability Proofs.
ABSTRACT We formalize in this paper a key property of asserting clauses (the most common type of clauses learned by SAT solvers). We show that the formalized property, which is called em- powerment, is not exclusive to asserting clauses, and intro- duce a new class of learned clauses which can also be empow- ering. We show empirically that (1) the new class of clauses tends to be much shorter and induce further backtracks than asserting clauses and (2) an empowering subset of this new class of clauses significantly improves the performance of the Rsat solver on unsatisfiable problems.
Conference Paper: A SAT-based approach to solve the faculty course scheduling problem[Show abstract] [Hide abstract]
ABSTRACT: The faculty course scheduling problem is concerned with assigning time slots to courses while taking into consideration some departmental and university constraints and instructor preferences. The problem also aims at optimizing the performance criteria and distributing the courses evenly among the available time slots. The problem is a classic scheduling problem and has received some research during the past few years due to its wide use in academic institutions. Most of the proposed solutions were based on local search techniques. In this paper, we present a complete approach to solving the course scheduling problem using Boolean Satisfiability (SAT). Experimental results show the effectiveness of the proposed approach.AFRICON 2013; 09/2013
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ABSTRACT: We offer a new understanding of some aspects of practical SAT-solvers that are based on DPLL with unit-clause propagation, clause-learning, and restarts. We do so by analyzing a concrete algorithm which we claim is faithful to what practical solvers do. In particular, before making any new decision or restart, the solver repeatedly applies the unit-resolution rule until saturation, and leaves no component to the mercy of non-determinism except for some internal randomness. We prove the perhaps surprising fact that, although the solver is not explicitly designed for it, with high probability it ends up behaving as width-k resolution after no more than O(n^2k+2) conflicts and restarts, where n is the number of variables. In other words, width-k resolution can be thought of as O(n^2k+2) restarts of the unit-resolution rule with learning.Journal of Artificial Intelligence Research 01/2014; 40. DOI:10.1613/jair.3152 · 0.90 Impact Factor
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ABSTRACT: The propositional satisfiability SAT problem is one of the most fundamental problems in computer science. SAT solvers have been successfully applied to a wide range of practical applications, including hardware model checking, software model finding, equivalence checking, and planning, among many others. Empirical research has been very fruitful for the development of efficient methods for SAT problems, such as classical Davis-Putnam method, greedy SAT GSAT method and neural network SAT method. This paper gives a survey about the methods used for solving the SAT problems with an emphasis on surveying the local search algorithms.International Journal of Advanced Intelligence Paradigms 09/2013; 5(3):233-256. DOI:10.1504/IJAIP.2013.056447