CMPSC 442: Homework 4 [100 points]
TO SUBMIT HOMEWORK
To submit homework for a given homework assignment:
1. You *must* download the homework template file from Canvas, located in Files/Homework Templates and
Pdfs, and modify this file to complete your homework. Each template file is a python file that will give you a
headstart in creating your homework python script. For a given homework number N, the template file
name is homeworkN-cmpsc442.py. The template for homework #4 is homework4-cmpsc442.py. IF YOU
DO NOT USE THE CORRECT TEMPLATE FILE, YOUR HOMEWORK CANNOT BE GRADED AND YOU
WILL RECEIVE A ZERO.
2. You *must* rename the file by replacing the file root using your PSU id that consists of your initials
followed by digits. This is the same as the part of your PSU email that precedes the “@” sign. For example,
your instructor’s email is [email protected], and her PSU id is rjp49. Your homework files for every
assignment will have the same name, e.g., rjp49.py. IF YOU DO NOT RENAME YOUR HOMEWORK FILE
CORRECTLY, IT WILL NOT BE GRADED AND YOU WILL RECEIVE A ZERO. Do not be alarmed if you
upload a revision, and it is renamed to include a numeric index, e.g., rjp49-1.py or rjp49-2.py. We can
handle this automatic renaming.
3. You *must* upload your homework to the assignments area in Canvas by 11:59 pm on the due date. You will
have two opportunities (NO MORE) to submit up to two days late. IF YOU DO NOT UPLOAD YOUR
HOMEWORK TO THE ASSIGNMENT FOLDER BY THE DUE DATE (OR THE TWO-DAY GRACE
PERIOD IN SOME CASES), IT CANNOT BE GRADED AND YOU WILL RECEIVE A ZERO.
In this assignment, you will implement three inference algorithms for the popular puzzle game Sudoku.
A skeleton *.py file homework4‑cmpsc442.py containing empty definitions for each question has been provided
(see above). Since portions of this assignment will be graded automatically, none of the names or function
signatures in this file should be modified. However, you are free to introduce additional variables or functions if
You may import definitions from any standard Python library, and are encouraged to do so in case you find
yourself reinventing the wheel. If you are unsure where to start, consider taking a look at the data structures and
functions defined in the collections, copy, and itertools modules.
You will find that in addition to a problem specification, most programming questions also include one or two
examples from the Python interpreter. IN ADDITION TO PERFORMING YOUR OWN TESTING, YOU ARE
STRONGLY ENCOURAGED TO VERIFY THAT YOUR CODE GIVES THE EXPECTED OUTPUT FOR THESE
EXAMPLES BEFORE SUBMITTING.
It is highly recommended that you follow the Python style guidelines set forth in PEP 8, which was written in part
by the creator of Python. However, your code will not be graded for style.
1. Sudoku [95 points]
In the game of Sudoku, you are given a partially-filled 9 x 9 grid, grouped into a 3 x 3 grid of 3 x 3 blocks. The
objective is to fill each square with a digit from 1 to 9, subject to the requirement that each row, column, and
block must contain each digit exactly once.
In this section, you will implement the AC-3 constraint satisfaction algorithm for Sudoku, along with two
extensions that will combine to form a complete and efficient solver.
A number of puzzles have been made available on Canvas in the Homework Tempates and Pdfs file for testing,
An easy-difficulty puzzle: hw4‑easy.txt.
Four medium-difficulty puzzles: hw4‑medium1.txt, hw4‑medium2.txt, hw4‑medium3.txt, and
Two hard-difficulty puzzles: hw4‑hard1.txt and hw4‑hard2.txt.
The examples in this section assume that these puzzle files have been placed in a folder named sudoku located in
the same directory as the homework file.
An example puzzle from the Daily Pennsylvanian, available as hw4‑medium1.txt, is depicted below.
Three Sudoku Puzzles
1. [3 points] In this section, we will view a Sudoku puzzle not from the perspective of its grid layout, but
more abstractly as a collection of cells. Accordingly, we will represent it internally as a dictionary mapping
from cells, i.e. (row, column) pairs, to sets of possible values.
In the Sudoku class, write an initialization method __init__(self, board) that stores such a mapping for
future use. Also write a method get_values(self, cell) that returns the set of values currently available at
a particular cell.
In addition, write a function read_board(path) that reads the board specified by the file at the given path
and returns it as a dictionary. Sudoku puzzles will be represented textually as 9 lines of 9 characters each,
corresponding to the rows of the board, where a digit between “1” and “9” denotes a cell containing a fixed
value, and an asterisk “*” denotes a blank cell that could contain any digit.
>>> b = read_board(“sudoku/hw4‑medium1.txt”)
>>> Sudoku(b).get_values((0, 0))
set([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> b = read_board(“sudoku/hw4‑medium1.txt”)
>>> Sudoku(b).get_values((0, 1))
2. [2 points] Write a function sudoku_cells() that returns the list of all cells in a Sudoku puzzle as
(row, column) pairs. The line CELLS = sudoku_cells() in the Sudoku class then creates a class-level
constant Sudoku.CELLS that can be used wherever the full list of cells is needed. Although the function
sudoku_cells() could still be called each time in its place, that approach results in a large amount of
repeated computation and is therefore highly inefficient. The ordering of the cells within the list is not
important, as long as they are all present.
[(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), …, (8, 5), (8, 6), (8, 7), (8, 8)]
3. [5 points] Write a function sudoku_arcs() that returns the list of all arcs between cells in a Sudoku puzzle
corresponding to inequality constraints. In other words, each arc should be a pair of cells whose values
cannot be equal in a solved puzzle. The arcs should be represented a two-tuples of cells, where cells
themselves are (row, column) pairs. The line ARCS = sudoku_arcs() in the Sudoku class then creates a classlevel constant Sudoku.ARCS that can be used wherever the full list of arcs is needed. The ordering of the arcs
within the list is not important, as long as they are all present.
>>> ((0, 0), (0, 8)) in sudoku_arcs()
>>> ((0, 0), (8, 0)) in sudoku_arcs()
>>> ((0, 8), (0, 0)) in sudoku_arcs()
>>> ((0, 0), (2, 1)) in sudoku_arcs()
>>> ((2, 2), (0, 0)) in sudoku_arcs()
>>> ((2, 3), (0, 0)) in sudoku_arcs()
4. [10 points] In the Sudoku class, write a method remove_inconsistent_values(self, cell1, cell2) that
removes any value in the set of possibilities for cell1 for which there are no values in the set of possibilities
for cell2 satisfying the corresponding inequality constraint. Each cell argument will be a (row, column)
pair. If any values were removed, return True; otherwise, return False.
Hint: Think carefully about what this exercise is asking you to implement. How many values can be
removed during a single invocation of the function?
>>> sudoku = Sudoku(read_board(“sudoku/hw4‑easy.txt”)) # See below for a picture.
>>> sudoku.get_values((0, 3))
set([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> for col in [0, 1, 4]:
… removed = sudoku.remove_inconsistent_values((0, 3), (0, col))
… print removed, sudoku.get_values((0, 3))
True set([1, 2, 3, 4, 5, 6, 7, 9])
True set([1, 3, 4, 5, 6, 7, 9])
False set([1, 3, 4, 5, 6, 7, 9])
5. [15 points] In the Sudoku class, write a method infer_ac3(self) that runs the AC-3 algorithm on the
current board to narrow down each cell’s set of values as much as possible. Although this will not be
powerful enough to solve all Sudoku problems, it will produce a solution for easy-difficulty puzzles such as
the one shown below. By “solution”, we mean that there will be exactly one element in each cell’s set of
possible values, and that no inequality constraints will be violated.
6. [30 points] Consider the outcome of running AC-3 on the medium-difficulty puzzle shown below.
Although it is able to determine the values of some cells, it is unable to make significant headway on the
However, if we consider the possible placements of the digit 7 in the upper-right block, we observe that the
7 in the third row and the 7 in the final column rule out all but one square, meaning we can safely place a 7
in the indicated cell despite AC-3 being unable to make such an inference.
In the Sudoku class, write a method infer_improved(self) that runs this improved version of AC-3, using
infer_ac3(self) as a subroutine (perhaps multiple times). You should consider what deductions can be
made about a specific cell by examining the possible values for other cells in the same row, column, or
block. Using this technique, you should be able to solve all of the medium-difficulty puzzles.
7. [30 points] Although the previous inference algorithm is an improvement over the ordinary AC-3
algorithm, it is still not powerful enough to solve all Sudoku puzzles. In the Sudoku class, write a method
infer_with_guessing(self) that calls infer_improved(self) as a subroutine, picks an arbitrary value for a
cell with multiple possibilities if one remains, and repeats. You should implement a backtracking search
which reverts erroneous decisions if they result in unsolvable puzzles. For efficiency, the improved
inference algorithm should be called once after each guess is made. This method should be able to solve all
of the hard-difficulty puzzles, such as the one shown below.
Infer with Guessing
2. Feedback [5 points]
1. [1 point] Approximately how long did you spend on this assignment?
2. [2 points] Which aspects of this assignment did you find most challenging? Were there any significant
3. [2 points] Which aspects of this assignment did you like? Is there anything you would have changed?