You are to create a program in Python $3$ that performs the following $($you can use another language you are

comfortable with. In either case, make sure to pay close attention to requirements$)$:

$1.$ Loads the mp$1$input.txt file from the current directory. This represents the starting state of the sliding

puzzle. The format of this file is composed of $3$ rows of $3$ values, each value separated by a single space.

The values are the integers $0$ through $8$ that represent the puzzle. The $0$ integer represents an empty space

$($no tile$).$ Here is an example of the input file contents:

$312$

$475$

$068$

$2.$ Displays heading information to the screen:

Artificial Intelligence

MP$1$: A$*$ for Sliding Puzzle

SEMESTER: $[$put semester and year here$]$

NAME: $[$your name here$]$

$3.$ Executes the A$*$ algorithm with the Manhattan distance heuristic $($as discussed in the textbook$).$ The goal

state is this configuration:

$012$

$345$

$678$

$4.$ Shows the solution in form of the puzzle configurations after each move, the move number, and the

action taken. This format should match the sample output shown on the last page.

$5.$ Displays the number of states that A$*$ had to visit in order to get to the solution.

Additional Requirements

$1.$ The name of your source code file should be mp$1.$py$.$ All your code should be within a single file.

$2.$ You can only import numpy and queue packages.

$3.$ Your code should follow good coding practices, including good use of whitespace and use of both inline

and block comments.

$4.$ You need to use meaningful identifier names that conform to standard naming conventions.

$5.$ At the top of each file, you need to put in a block comment with the following information: your name,

date, course name, semester, and assignment name.

$6.$ The output should exactly match the sample output shown on the last page. Note that for a different

input state, the output may be different. I will be testing on a different input than shown in the sample.

HINTS

$$ It$$s easiest to implement A$*$ if you first define the class of PuzzleState objects. These should contain at

least the following: the puzzle configuration $($a numpy $2$d array$),$ the g$/$h$/$f costs, predecessor state

reference, and the action that was taken to get to this state from the predecessor.

$$ Use Python$$s built$-$in PriorityQueue from the queue package to implement the frontier $($open$)$ set. Make

sure to implement the $\_\_$lt$\_\_$ method in your PuzzleState class.

$$ Use Python$$s built$-$in set object to implement the closed set. You will need to make sure you implement

the $\_\_$hash$\_\_$ method in your PuzzleState class to do this.

Artificial Intelligence

MP$1$: A$*$ for Sliding Puzzle

SEMESTER: $[$put semester and year here$]$

NAME: $[$your name here$]$

START

$[[312]$

$[475]$

$[068]]$

Move $1$ ACTION: right

$[[312]$

$[475]$

$[608]]$

Move $2$ ACTION: up

$[[312]$

$[405]$

$[678]]$

Move $3$ ACTION: left

$[[312]$

$[045]$

$[678]]$

Move $4$ ACTION: up

$[[012]$

$[345]$

$[678]]$

Number of states visited $=10$

i wanna code for this $.$