Literature Review on Constraint Satisfaction Problems Solving.

Abstract

A class of issues known as Constraint Satisfaction issues (CSPs) require that a set of variables be given values from their respective domains in a way that satisfies all of the given constraints. Many applications in computer science, operations research, and artificial intelligence, such as resource allocation, scheduling, and planning, revolve around these issues. This review offers a thorough rundown of the main approaches used to solve CSPs, including heuristic techniques, contemporary and classical algorithms, and underlying theories. It explores specific CSPs and the methods created to solve them, looks at real-world uses, and talks about potential paths for further study. The paper focuses on how the efficiency and scalability of CSP solvers are continuously being improved by developments in methods like constraint programming, SAT solvers, and machine learning