Structure-Based Satisfiability Checking

Constraint satisfaction deals with developing automated techniques for solving computationally hard problems in a declarative fashion. This note summarizes a dissertation that focuses on search-based methods for the propositional satisfiability problem (SAT). As methods for propositional satisfiability checking have rapidly progressed during the last 15 years, implementations of decision procedures for SAT, so called SAT solvers [3], have been found to be extremely efficient as back-end search engines in solving large industrial-scale combinatorial problems. Since SAT solvers have become a standard tool for attacking various real-world problem instances of increasing size and difficulty, there is a demand for more and more robust and efficient solvers. For understanding the successes (and failures) of SAT solvers in specific problem domains, it is important to investigate how different types of structural properties of SAT instances are related to the efficiency of solving the instances with different SAT-based constraint satisfaction techniques. This is the underlying motivation for the thesis. The emphasis of the thesis is on search-based SAT solving techniques for solving structured real-world problems. The work focuses on the analysis and development of both complete search (DPLL and Clause Learning (CL)) and stochastic local search (SLS) methods for SAT. The thesis, available online as [5], consists of an overview and seven published articles [4,6,7,9,10,11, 12].