Code to solve the N-puzzle game (best known as the 8 puzzle game) using a min priority queue and the A* search algorithm. This problem is a classic problem in artificial intelligence

Use any board in the specified fortmat and the program will solve it (if it has a solution) and show you the steps get the solution:

Example:

The repository also contains the implementetions of a min and max priority queues with useful comments if you want to know how these algorithms work. The solver.py can work using either: the included minPQ.py or using the min priority queue from the python library heapq. Read the comments inside the files for further information

You can create your own boards using either: the included random generator in the solver.py file

(a snippet of the code inside solver.py)

// code away!
n = 3   # size of the board
a = list(random.sample(range(n*n),n*n))  
blocks = [[a[n*i+j] for j in range(n)] for i in range(n) ]

where n is the size of the board. If you choose this option, change the value of n or you can create your own files in .txt formst and save them in the source_data folder. To solve those board you can use the following part of the code (also included in solver.py):

(a snippet of the code inside solver.py)

data_folder = Path("source_data/")
file_to_open = data_folder / "puzzle05.txt"
f = open(file_to_open)
n = int(f.readline())
blocks = [[ int(el) for el in line.split()] for line in f ]

To know how to build your own boards in the right format, read the comments inside solver.py. Uncomment any one of above lines of code depending in how you want to supply the boards

• All the code and test cases required to get started, are already included in this repo

• will it be useful to look inside this code or it is of that kind of unreadable and ugly code?
  • Yes!!! It will be helpful to look inside, I try to write understandable code to make it accessible. Inside there are useful comments explaining how the code works and it has some cool "plain text art" to illustrate some key concepts. If you want to learn how to do similar things (solve path finding or do optimization) you should give it a try.

Reach out to me at one of the following places!

• Coming soon...

• MIT license