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CMPSC 442: Homework 2 [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 modify this file to complete your homework. For this homework (Homework 2), you will also need the
homework2_lights_out_gui.py file from Files/Homework Templates. 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. For example, the template for homework #2 is
homework2-compsc442.py. IF YOU DO NOT USE THE CORRECT TEMPLATE FILE, YOUR HOMEWORK
CANNOT BE GRADED AND YOU WILL RECEIVE A ZERO.

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CMPSC 442: Homework 2 [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 modify this file to complete your homework. For this homework (Homework 2), you will also need the
homework2_lights_out_gui.py file from Files/Homework Templates. 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. For example, the template for homework #2 is
homework2-compsc442.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 correct assignments area in Canvas by 11:59 pm on the due date.
(Apologies for the misinformation on the Homework 1 page.) Do not confuse assignments with each other.
IF YOU DO NOT UPLOAD YOUR HOMEWORK TO THE CORRECT ASSIGNMENT FOLDER, IT WILL
NOT BE GRADED AND YOU WILL RECEIVE A ZERO.
Instructions
In this assignment, you will explore three classic puzzles from the perspective of uninformed search.
A skeleton file homework2‑cmpsc442.py containing empty definitions for each question has been provided. 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 needed.
You may import definitions from any standard Python library, and are encouraged to do so in cases where 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, itertools, math, and random modules.
You will find that in addition to a problem specification, most programming questions also include a pair of
examples from the Python interpreter. These are meant to illustrate typical use cases, and should not be taken as
comprehensive test suites.
You are strongly encouraged to 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. N­Queens [25 points]
In this section, you will develop a solver for the n-queens problem, wherein n queens are to be placed on an n x n
chessboard so that no pair of queens can attack each other. Recall that in chess, a queen can attack any piece that
lies in the same row, column, or diagonal as itself.
A brief treatment of this problem for the case where n = 8 is given in Section 3.2 of the textbook, which you may
wish to consult for additional information.
1. [5 points] Rather than performing a search over all possible placements of queens on the board, it is
sufficient to consider only those configurations for which each row contains exactly one queen. Without
taking any of the chess-specific constraints between queens into account, implement the pair of functions
num_placements_all(n) and num_placements_one_per_row(n) that return the number of possible
placements of n queens on an n x n board without or with this additional restriction. Think carefully about
why this restriction is valid, and note the extent to which it reduces the size of the search space. You should
assume that all queens are indistinguishable for the purposes of your calculations.
2. [5 points] With the answer to the previous question in mind, a sensible representation for a board
configuration is a list of numbers between 0 and n ­ 1, where the ith number designates the column of the
queen in row i for 0 ≤ i < n. A complete configuration is then specified by a list containing n numbers, and a partial configuration is specified by a list containing fewer than n numbers. Write a function n_queens_valid(board) that accepts such a list and returns True if no queen can attack another, or False otherwise. Note that the board size need not be included as an additional argument to decide whether a particular list is valid. >>> n_queens_valid([0, 0])
False
>>> n_queens_valid([0, 2])
True
>>> n_queens_valid([0, 1])
False
>>> n_queens_valid([0, 3, 1])
True
3. [15 points] Write a function n_queens_solutions(n) that yields all valid placements of n queens on an n x
n board, using the representation discussed above. The output may be generated in any order you see fit.
Your solution should be implemented as a depth-first search, where queens are successively placed in
empty rows until all rows have been filled. Hint: You may find it helpful to define a helper function
n_queens_helper(n, board) that yields all valid placements which extend the partial solution denoted by
board.
Though our discussion of search in class has primarily covered algorithms that return just a single solution,
the extension to a generator which yields all solutions is relatively simple. Rather than using a return
statement when a solution is encountered, yield that solution instead, and then continue the search.
>>> solutions = n_queens_solutions(4)
>>> next(solutions)
[1, 3, 0, 2]
>>> next(solutions)
[2, 0, 3, 1]
>>> list(n_queens_solutions(6))
[[1, 3, 5, 0, 2, 4], [2, 5, 1, 4, 0, 3],
[3, 0, 4, 1, 5, 2], [4, 2, 0, 5, 3, 1]]
>>> len(list(n_queens_solutions(8)))
92
2. Lights Out [40 points]
The Lights Out puzzle consists of an m x n grid of lights, each of which has two states: on and off. The goal of the
puzzle is to turn all the lights off, with the caveat that whenever a light is toggled, its neighbors above, below, to
the left, and to the right will be toggled as well. If a light along the edge of the board is toggled, then fewer than
four other lights will be affected, as the missing neighbors will be ignored.
In this section, you will investigate the behavior of Lights Out puzzles of various sizes by implementing a
LightsOutPuzzle class. Once you have completed the problems in this section, you can test your code in an
interactive setting using the provided GUI. See the end of the section for more details.
1. [2 points] A natural representation for this puzzle is a two-dimensional list of Boolean values, where True
corresponds to the on state and False corresponds to the off state. In the LightsOutPuzzle class, write an
initialization method __init__(self, board) that stores an input board of this form for future use. Also
write a method get_board(self) that returns this internal representation. You additionally may wish to
store the dimensions of the board as separate internal variables, though this is not required.
>>> b = [[True, False], [False, True]]
>>> p = LightsOutPuzzle(b)
>>> p.get_board()
[[True, False], [False, True]]
>>> b = [[True, True], [True, True]]
>>> p = LightsOutPuzzle(b)
>>> p.get_board()
[[True, True], [True, True]]
2. [3 points] Write a top-level function create_puzzle(rows, cols) that returns a new LightsOutPuzzle of
the specified dimensions with all lights initialized to the off state.
>>> p = create_puzzle(2, 2)
>>> p.get_board()
>>> p = create_puzzle(2, 3)
>>> p.get_board()
[[False, False], [False, False]] [[False, False, False],
[False, False, False]]
3. [5 points] In the LightsOutPuzzle class, write a method perform_move(self, row, col) that toggles the
light located at the given row and column, as well as the appropriate neighbors.
>>> p = create_puzzle(3, 3)
>>> p.perform_move(1, 1)
>>> p.get_board()
[[False, True, False],
[True, True, True ],
[False, True, False]]
>>> p = create_puzzle(3, 3)
>>> p.perform_move(0, 0)
>>> p.get_board()
[[True, True, False],
[True, False, False],
[False, False, False]]
4. [5 points] In the LightsOutPuzzle class, write a method scramble(self) which scrambles the puzzle by
calling perform_move(self, row, col) with probability 1/2 on each location on the board. This guarantees
that the resulting configuration will be solvable, which may not be true if lights are flipped individually.
Hint: After importing the random module with the statement import random, the expression
random.random() < 0.5 generates the values True and False with equal probability. 5. [2 points] In the LightsOutPuzzle class, write a method is_solved(self) that returns whether all lights on the board are off. >>> b = [[True, False], [False, True]]
>>> p = LightsOutPuzzle(b)
>>> p.is_solved()
False
>>> b = [[False, False], [False, False]]
>>> p = LightsOutPuzzle(b)
>>> p.is_solved()
True
6. [3 points] In the LightsOutPuzzle class, write a method copy(self) that returns a new LightsOutPuzzle
object initialized with a deep copy of the current board. Changes made to the original puzzle should not be
reflected in the copy, and vice versa.
>>> p = create_puzzle(3, 3)
>>> p2 = p.copy()
>>> p.get_board() == p2.get_board()
True
>>> p = create_puzzle(3, 3)
>>> p2 = p.copy()
>>> p.perform_move(1, 1)
>>> p.get_board() == p2.get_board()
False
7. [5 points] In the LightsOutPuzzle class, write a method successors(self) that yields all successors of the
puzzle as (move, new-puzzle) tuples, where moves themselves are (row, column) tuples. The second
element of each successor should be a new LightsOutPuzzle object whose board is the result of applying the
corresponding move to the current board. The successors may be generated in whichever order is most
convenient.
>>> p = create_puzzle(2, 2)
>>> for move, new_p in p.successors():
… print move, new_p.get_board()

(0, 0) [[True, True], [True, False]]
(0, 1) [[True, True], [False, True]]
(1, 0) [[True, False], [True, True]]
(1, 1) [[False, True], [True, True]]
>>> for i in range(2, 6):
… p = create_puzzle(i, i + 1)
… print len(list(p.successors()))

6
12
20
30
8. [15 points] In the LightsOutPuzzle class, write a method find_solution(self) that returns an optimal
solution to the current board as a list of moves, represented as (row, column) tuples. If more than one
optimal solution exists, any of them may be returned. Your solver should be implemented using a breadthfirst graph search, which means that puzzle states should not be added to the frontier if they have already
been visited, or are currently in the frontier. If the current board is not solvable, the value None should be
returned instead. You are highly encouraged to reuse the methods defined in the previous exercises while
developing your solution.
Hint: For efficient testing of duplicate states, consider using tuples representing the boards of the
LightsOutPuzzle objects being explored rather than their internal list­based representations. You will
then be able to use the built­in set data type to check for the presence or absence of a particular state in
near­constant time.
>>> p = create_puzzle(2, 3)
>>> for row in range(2):
… for col in range(3):
… p.perform_move(row, col)

>>> p.find_solution()
[(0, 0), (0, 2)]
>>> b = [[False, False, False],
… [False, False, False]]
>>> b[0][0] = True
>>> p = LightsOutPuzzle(b)
>>> p.find_solution() is None
True
Once you have implemented the functions and methods described in this section, you can play with an interactive
version of the Lights Out puzzle using the provided GUI by running the following command:
python homework2_lights_out_gui.py rows cols
The arguments rows and cols are positive integers designating the size of the puzzle.
In the GUI, you can click on a light to perform a move at that location, and use the side menu to scramble or solve
the puzzle. The GUI is merely a wrapper around your implementations of the relevant functions, and may
therefore serve as a useful visual tool for debugging.
3. Linear Disk Movement [30 points]
In this section, you will investigate the movement of disks on a linear grid.
The starting configuration of this puzzle is a row of L cells, with disks located on cells 0 through n ­ 1. The goal is
to move the disks to the end of the row using a constrained set of actions. At each step, a disk can only be moved
to an adjacent empty cell, or to an empty cell two spaces away, provided another disk is located on the
intervening square. Given these restrictions, it can be seen that in many cases, no movements will be possible for
the majority of the disks. For example, from the starting position, the only two options are to move the last disk
from cell n ­ 1 to cell n, or to move the second-to-last disk from cell n ­ 2 to cell n.
1. [15 points] Write a function solve_identical_disks(length, n) that returns an optimal solution to the
above problem as a list of moves, where length is the number of cells in the row and n is the number of
disks. Each move in the solution should be a two-element tuple of the form (from, to) indicating a disk
movement from the first cell to the second. As suggested by its name, this function should treat all disks as
being identical.
Your solver for this problem should be implemented using a breadth-first graph search. The exact solution
produced is not important, as long as it is of minimal length.
Unlike in the previous two sections, no requirement is made with regards to the manner in which puzzle
configurations are represented. Before you begin, think carefully about which data structures might be best
suited for the problem, as this choice may affect the efficiency of your search.
>>> solve_identical_disks(4, 2)
[(0, 2), (1, 3)]
>>> solve_identical_disks(5, 2)
[(0, 2), (1, 3), (2, 4)]
>>> solve_identical_disks(4, 3)
[(1, 3), (0, 1)]
>>> solve_identical_disks(5, 3)
[(1, 3), (0, 1), (2, 4), (1, 2)]
2. [15 points] Write a function solve_distinct_disks(length, n) that returns an optimal solution to the
same problem with a small modification: in addition to moving the disks to the end of the row, their final
order must be the reverse of their initial order. More concretely, if we abbreviate length as L, then a
desired solution moves the first disk from cell 0 to cell L ­ 1, the second disk from cell 1 to cell L ­ 2, \cdots,
and the last disk from cell n ­ 1 to cell L ­ n.
Your solver for this problem should again be implemented using a breadth-first graph search. As before, the
exact solution produced is not important, as long as it is of minimal length.
>>> solve_distinct_disks(4, 2)
[(0, 2), (2, 3), (1, 2)]
>>> solve_distinct_disks(5, 2)
[(0, 2), (1, 3), (2, 4)]
>>> solve_distinct_disks(4, 3)
[(1, 3), (0, 1), (2, 0), (3, 2), (1, 3),
(0, 1)]
>>> solve_distinct_disks(5, 3)
[(1, 3), (2, 1), (0, 2), (2, 4), (1, 2)]
4. 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
stumbling blocks?
3. [2 points] Which aspects of this assignment did you like? Is there anything you would have changed?