diyjac / aind-p1

Sudoku Solver AI Agent

Artificial Intelligence Nanodegree

Introductory Project: Diagonal Sudoku Solver

AIND-P1 is a project result submission for Udacity Diagonal Sudoku Solver project. It is a command line python program with an optional pygame interface, in which we will use elimination, only choice, naked-twin and search tree techniques with constraint propagation AI stratagy to solve the sudoku puzzle in all its forms including the diagonal sudoku with its new diagonal units A1 B2 C3 D4 E5 F6 G7 H8 I9 and A9 B8 C7 D6 E5 F4 G3 H2 I1. The code that implements the additional diagonal constraints is located in solution.py. More details about this diagonal sudoku solver implementation can be found here

Install

This project requires Python 3.

We recommend students install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project. Please try using the environment we provided in the Anaconda lesson of the Nanodegree.

Optional: Pygame

Optionally, you can also install pygame if you want to see our visualization. If you've followed our instructions for setting up our conda environment, you should be all set.

If not, please see how to download pygame here.

Code

• solutions.py - This code implements our solution.
• solution_test.py - Do not modify this. You can test your solution by running python solution_test.py.
• PySudoku.py - Do not modify this. This is code for visualizing your solution.
• visualize.py - Do not modify this. This is code for visualizing your solution.

Visualizing

To visualize the solution, we only assign values to the values_dict using the assign_values function provided in solution.py. This will allow us to see and track the AI agents behavior when solving the sudoku puzzle:


Contributing

No futher updates nor contributions are requested. This project is static.

License

AIND-P1 results are released under MIT License