Constraint Satisfaction Problem Solver

Overview

The Constraint Satisfaction Problem (CSP) Solver demonstrates backtracking and constraint propagation techniques for solving problems with variables, domains, and constraints. Watch as the solver assigns values to variables while maintaining consistency with all constraints. Includes heuristics like MRV (minimum remaining values) and forward checking. Perfect for understanding AI search, scheduling, and logic puzzles like Sudoku.

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Tips

• CSPs consist of variables, domains (possible values), and constraints between variables
• Backtracking tries values and backtracks when constraints are violated
• MRV heuristic: choose variable with fewest legal values remaining
• Forward checking: when assigning a variable, remove inconsistent values from other domains
• Arc consistency is stronger: enforce consistency on all pairs of variables
• Try classic CSP problems: graph coloring, N-Queens, map coloring, Sudoku
• Watch the search tree visualization to see backtracking in action
• Heuristics dramatically reduce search space compared to naive backtracking