## Description

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?