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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?

Constraint propagation is the applying of local constraints to reduce a search space. In the case of Sudoku, assuming we only look for twins with two possible values for their box, we can't start off with applying naked_twins; we have to reduce the possibilities of the board first. After we've applied the eliminate and only_choice constraints, we've sufficiently reduced the amount of possibilities per box to apply naked_twins. This constraint we can apply when there are two boxes that both contain the same two values as possibilities. This means that we can remove these two values as possibilities for their peers, thus further reducing the number of possible boards we have to search through to find a solution.

Question 2 (Diagonal Sudoku)

Q: How do we use constraint propagation to solve the diagonal sudoku problem?

The diagonal-sudoku problem in this case is another "constraint" we can use to arrive closer to an eventual solution. I put constraint in quotes as it's not a being coded as a function like our other constraints, like only_choice, eliminate, and naked_twins. It actually bolsters those constraint functions by applying another set of boxes to be viewed as peers giving us the ability to enforce those constraints over a larger set of the board.


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