Implement AC-3 and backtracking algorithms to solve Sudoku puzzles. The objective of the game is to fill a 9 x 9 grid with numerical digits so that each column, each row, and each of the 3 x 3 sub-grids (also called boxes) contains one of all the digits 1 through 9.

• 81 variables in total, and tiles to be filled with digits.
• Variables A1 through A9 are the top row (form left to right)
• I1 through I9 are the bottom row

Write a driver.py that intelligently solves Sudoku puzzles

To run:

python3 driver.py <input_string>

Provided beforehand is sudoku_start.txt, containing hundreds of Sudoku puzzles. Each Sudoku puzzle is represented as a single line of text, starting from top-left corner of the board and enumerates the digits in each tile, row by row. 0 is used to indicate tiles that have not yet been filled.

When executed with an input string, the program will generate an output.txt containing a single line of text representing the finished Sudoku board and the algorithm name which solved the Sudoku board.

The program can be solved extensively using sudokus_finish.txt which contain the solved versions of all the same puzzles.

Implement the AC-3 Algorithm. Of the solutions in sudokus_finish.txt, there are only 2/400 Sudoku board which can be solved with AC3 alone.

Implement backtracking using the minimum remaining value heuristic. The order of values to be attempted for each variable is add here. When a variable is assigned, apply forward checking to reduce variable domains.

1. This should show a comparison of how powerful BTS is compared to AC3. Execute the AC-3 algorithm before Backtracking Search algorithm.
2. The program will be tested against 20 test cases.
3. Correctly implemented backtracking should take well under a minute per puzzle.