Solving the Satisfiability Problem Using Finite Learning Automata

Abstract

A large number of problems that occur in knowledge-representation, learning, VLSI-design, and other areas of artificial intelligence, are essentially satisfiability problems. The satisfiability problem refers to the task of finding a truth assignment that makes a Boolean expression true. The growing need for more efficient and scalable algorithms has led to the development of several SAT solvers. This paper reports the first approach based on combining finite learning automata with metaheuristics. In brief, we introduce a new algorithm that combines finite learning automata with traditional random walk. Furthermore, we present a detailed comparative analysis of the new algorithm's performance, using a benchmark set containing instances from randomized distributions, as well as SAT-encoded problems from various domains.