A New Method for Solving Constraint Satisfaction Problems

Many important problems in Artificial Intelligence can be defined as Constraint Satisfaction Problems (CSP). These types of problems are defined by a limited set of variables, each having a limited domain and a number of Constraints on the values of those variables (these problems are also called Consistent Labeling Problems (CLP), in which “Labeling means assigning a value to a variable.) Solution to these problems is a set of unique values for variables such that all the problem constraints are satisfied. Several search algorithms have been proposed for solving these problems, some of which reduce the need for backtracking by doing some sort of looking to future, and produce more efficient solutions. These are the so-called Forward Checking (FC), Partially Lookahead (PL), and Fully Lookahead (FL) algorithms. They are different in terms of the amount of looking to the future, number of backtracks that are performed, and the quality of the solution that they find. In this paper, we propose a new search algorithm we call Modified Fully Lookahead (MFL) which is Shown to be more efficient than the original Fully Lookahead algorithm