Combining finite learning automata with GSAT for the satisfiability problem

• Published in: Engineering applications of artificial intelligence Vol. 23; no. 5; pp. 715 - 726
• Main Authors: Bouhmala, Noureddine, Granmo, Ole-Christoffer
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2010
• Subjects: Algorithms; Artificial intelligence; Automation; Combinatorial optimization; Expert systems; GSAT; Learning; Learning automata; Mathematical analysis; Random walk; Satisfiability problem; Solvers; Tasks; Satisfiability problem; Learning automata; Combinatorial optimization; Random walk; GSAT
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2010.01.009

A large number of problems that occur in knowledge-representation, learning, very large scale integration technology (VLSI-design), and other areas of artificial intelligence, are essentially satisfiability problems. The satisfiability problem refers to the task of finding a satisfying assignment that makes a Boolean expression evaluate to True. The growing need for more efficient and scalable algorithms has led to the development of a large number of SAT solvers. This paper reports the first approach that combines finite learning automata with the greedy satisfiability algorithm (GSAT). In brief, we introduce a new algorithm that integrates finite learning automata and traditional GSAT used with random walk. Furthermore, we present a detailed comparative analysis of the new algorithm's performance, using a benchmark set containing randomized and structured problems from various domains.