Constraint Satisfaction Problems (CSPs) are a fundamental concept in artificial intelligence (AI) where a solution must satisfy a set of constraints. A CSP consists of variables, domains (possible values for each variable), and constraints that specify allowable combinations of values. Common examples include scheduling, map coloring, and Sudoku. AI techniques such as backtracking, constraint propagation, and heuristics like the minimum remaining value (MRV) are used to solve CSPs efficiently. These problems are particularly useful in fields like operations research, robotics, and natural language processing, where structured decision-making is required under predefined limitations.
