Cse 402 offline b2

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CSE 402: Offline Assignment 3
Solve 8 Puzzle
Write a program to solve the 8-puzzle problem (and its natural generalizations) using the A*
search algorithm.
The problem. The 8-puzzle is a puzzle invented and popularized by Noyes Palmer Chapman in
the 1870s. It is played on a 3-by-3 grid with 8 square blocks labeled 1 through 8 and a blank
square. Your goal is to rearrange the blocks so that they are in order, using as few moves as
possible. You are permitted to slide blocks horizontally or vertically into the blank square. The
following shows a sequence of legal moves from an initial board (left) to the goal board (right).
1 3 1 3 1 2 3 1 2 3 1 2 3
4 2 5 => 4 2 5 => 4 5 => 4 5 => 4 5 6
7 8 6 7 8 6 7 8 6 7 8 6 7 8
initial 1 left 2 up 5 left goal
Best-first search. Now, we describe a solution to the problem that illustrates a general artificial
intelligence methodology known as the A* search algorithm. We define a search node of the
game to be a board, the number of moves made to reach the board, and the previous search node.
First, insert the initial search node (the initial board, 0 moves, and a null previous search node)
into a priority queue. Then, delete from the priority queue the search node with the minimum
priority, and insert onto the priority queue all neighboring search nodes (those that can be
reached in one move from the dequeued search node). Repeat this procedure until the search
node dequeued corresponds to a goal board. The success of this approach hinges on the choice of
priority function for a search node. We consider two priority functions:
 Hamming priority function. The number of blocks in the wrong position, plus the number
of moves made so far to get to the search node. Intutively, a search node with a small
number of blocks in the wrong position is close to the goal, and we prefer a search node
that have been reached using a small number of moves.
 Manhattan priority function. The sum of the Manhattan distances (sum of the vertical and
horizontal distance) from the blocks to their goal positions, plus the number of moves
made so far to get to the search node.
For example, the Hamming and Manhattan priorities of the initial search node below are 5 and
10, respectively.
8 1 3 1 2 3 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
4 2 4 5 6 ---------------------- ----------------------
7 6 5 7 8 1 1 0 0 1 1 0 1 1 2 0 0 2 2 0 3
initial goal Hamming = 5 + 0 Manhattan = 10 + 0

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We make a key oberservation: To solve the puzzle from a given search node on the priority
queue, the total number of moves we need to make (including those already made) is at least its
priority, using either the Hamming or Manhattan priority function. (For Hamming priority, this is
true because each block that is out of place must move at least once to reach its goal position. For
Manhattan priority, this is true because each block must move its Manhattan distance from its
goal position. Note that we do not count the blank square when computing the Hamming or
Manhattan priorities.) Consequently, when the goal board is dequeued, we have discovered not
only a sequence of moves from the initial board to the goal board, but one that makes the fewest
number of moves. (Challenge for the mathematically inclined: prove this fact.)
A critical optimization. Best-first search has one annoying feature: search nodes corresponding
to the same board are enqueued on the priority queue many times. To reduce unnecessary
exploration of useless search nodes, when considering the neighbors of a search node, don't
enqueue a neighbor if its board is the same as the board of the previous search node.
8 1 3 8 1 3 8 1 8 1 3 8 1 3
4 2 4 2 4 2 3 4 2 4 2 5
7 6 5 7 6 5 7 6 5 7 6 5 7 6
previous search node neighbor neighbor neighbor
(disallow)
Detecting unsolvable puzzles. Not all initial boards can lead to the goal board by a sequence of
legal moves, including the two below:
1 2 3 1 2 3 4
4 5 6 5 6 7 8
8 7 9 10 11 12
13 15 14
unsolvable
unsolvable
To detect such situations, use the fact that boards are divided into two equivalence classes with
respect to reachability: (i) those that lead to the goal board and (ii) those that cannot lead to the
goal board. Moreover, we can identify in which equivalence class a board belongs without
attempting to solve it.
 Odd board size. Given a board, an inversion is any pair of blocks i and j where i < j but i
appears after j when considering the board in row-major order (row 0, followed by row 1,
and so forth).

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If the board size N is an odd integer, then each legal move changes the number of
inversions by an even number. Thus, if a board has an odd number of inversions, then it
cannot lead to the goal board by a sequence of legal moves because the goal board has an
even number of inversions (zero).
The converse is also true: if a board has an even number of inversions, then it can lead to
the goal board by a sequence of legal moves.

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 Even board size. If the board size N is an even integer, then the parity of the number of
inversions is not invariant. However, the parity of the number of inversions plus the row
of the blank square is invariant: each legal move changes this sum by an even number. If
this sum is even, then it cannot lead to the goal board by a sequence of legal moves; if
this sum is odd, then it can lead to the goal board by a sequence of legal moves.
.
Board and Solver data types. Organize your program by creating a class Board with the
following API:
public class Board {
public Board(int[][] blocks) // construct a board from an N-by-N
//array of blocks
// (where blocks[i][j] = block in
//row i, column j)
public int size() // board size N
public int hamming() // number of blocks out of place

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public int manhattan() // sum of Manhattan distances
between blocks and goal
public boolean isGoal() // is this board the goal board?
public boolean isSolvable() // is the board solvable?
public boolean equals(Object y) // does this board equal y?
public Iterable<Board> neighbors() // all neighboring boards
public String toString() // string representation of the
//board (in the output format specified below)
public static void main(String[] args) // unit test
}
and a class Solver with the following API:
public class Solver {
public Solver(Board initial) // find a solution to the initial
// board (using the A* algorithm)
public int moves() // min number of moves to solve
//initial board
public Iterable<Board> solution() // sequence of boards in a
// shortest solution
public static void main(String[] args) // solve a slider puzzle
//(code given below)
}
Throw an java.lang.IllegalArgumentException if the initial board is not solvable. To
implement the A* algorithm, you will need a priority queue data structure. You are free to
download or implement codes of a priority queue data structure.
Solver test client. Use the following test client to read a puzzle from a file (specified as a
command-line argument) and print the solution to standard output.
public static void main(String[] args) {
// create initial board from file
In in = new In(args[0]);
int N = in.readInt();
int[][] blocks = new int[N][N];
for (int i = 0; i < N; i++)
for (int j = 0; j < N; j++)
blocks[i][j] = in.readInt();
Board initial = new Board(blocks);
// check if puzzle is solvable; if so, solve it and output solution
if (initial.isSolvable()) {
Solver solver = new Solver(initial);
System.out.println("Minimum number of moves = " + solver.moves());
for (Board board : solver.solution())
System.out.println(board);
}
// if not, report unsolvable
else {
System.out.println("Unsolvable puzzle");

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}
}
Sample inputs and outputs.
Input:
0 1 3
4 2 5
7 8 6
Output:
Minimum number of moves = 4
0 1 3
4 2 5
7 8 6
1 0 3
4 2 5
7 8 6
1 2 3
4 0 5
7 8 6
1 2 3
4 5 0
7 8 6
1 2 3
4 5 6
7 8 0
Input:
1 2 3
4 5 6
8 7 0
Output:
Unsolvable puzzle
Your program should work correctly for arbitrary N-by-N boards (for any 1 ≤ N < 128), even if it
is too slow to solve some of them in a reasonable amount of time.
Deliverables. Submit the files Board.java and Solver.java (with the Manhattan priority).

Cse 402 offline b2

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Cse 402 offline b2