Artificial Intelligence Nanodegree

Introductory Project: Diagonal Sudoku Solver

Question 1 (Naked Twins)

Q: How do we use constraint propagation to solve the naked twins problem?
A: We solve the naked twins problem by enforcing the constraint that no squares outside the two naked twins squares can contain the twin values. The way I implemented this in my code is as follows:

• For every unit in the puzzle, find out the boxes with only two-digit values (eg. '12', '23' etc.)
• Find the naked twins from among these boxes i.e. boxes with same two-digit values and pair them up like [box1, box2].
• For every naked twin pair, replace from their peers the naked twin digits such that no box outside of these naked twins contains the twin values.
• Repeat this process for the next unit.

Question 2 (Diagonal Sudoku)

Q: How do we use constraint propagation to solve the diagonal sudoku problem?
A: We used constraint propagation to solve the diagonal sudoku by adding the diagonals to the set of constraints. Specifically, I used the following steps:

• Create a list of all the diagonals (primary and secondary), and add it to the list unitlist.
• Since the unitlist is used in the creation of units and peers, every item of the diagonal list shows up as a constraint while trying to solve the puzzle using eliminate() and only_choice() methods.

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 your 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

• solution.py - You'll fill this in as part of your 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 your solution, please only assign values to the values_dict using the assign_value function provided in solution.py

Submission

Before submitting your solution to a reviewer, you are required to submit your project to Udacity's Project Assistant, which will provide some initial feedback.

The setup is simple. If you have not installed the client tool already, then you may do so with the command pip install udacity-pa.

To submit your code to the project assistant, run udacity submit from within the top-level directory of this project. You will be prompted for a username and password. If you login using google or facebook, visit this link for alternate login instructions.

This process will create a zipfile in your top-level directory named sudoku-.zip. This is the file that you should submit to the Udacity reviews system.