Abstract
The satisfiability problem (SAT) is one of the classical and also most important problems of the theoretical computer science and has a direct bearing on numerous practical cases. It is one of the most prominent problems in artificial intelligence and has important applications in many fields, such as hardware and software verification, test-case generation, AI planning, scheduling, and data structures that allow efficient implementation of search space pruning. In recent years, there has been a huge development in SAT solvers, especially CDCL-based solvers (Conflict-Driven Clause-Learning) for propositional logic formulas. The goal of this paper is to design and implement a simple but effective system for random generation of long and complex logical formulas with a variety of difficulties encoded inside. The resulting logical formulas, i.e. problem instances, could be used for testing existing SAT solvers. The entire system would be widely available as a web application in the client-server architecture. The proposed system enables generation of syntactically correct logical formulas with a random structure, encoded in a manner understandable to SAT Solvers. Logical formulas can be presented in different formats. A number of parameters affect the form of generated instances, their complexity and physical dimensions. The randomness factor can be entered to every generated formula. The developed application is easy to modify and open for further extensions. The final part of the paper describes examples of solvers’ tests of logical formulas generated by the implemented generator.
Highlights
Satisfiability problem is a classical problem of theoretical computer science which finds more and more practical applications [1]
The paper reports on the creation of the system for automatic generation of logical formulas in CNF form in DIMACS format
This system was designed as a service which, thanks to the web application, can be widely accessible
Summary
Satisfiability problem is a classical problem of theoretical computer science which finds more and more practical applications [1]. In recent years, there has been an enormous progress made when it comes to SAT solvers It was the result of finding numerous effective heuristics, which enable finding satisfiable solutions in a formula. The progress in question (regarding the CNF format) historically results from using, in the first place, a strategy for finding substitutions known as CDCL, which is Conflict-Driven Clause Learning. Those solvers may find a solution for an average problem of the 50,000 variables on an average computing device in a few seconds. The present work discusses the results of a basic version of the application prepared as part of [2], since when the system itself has been modified and is available as a web application**
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