Competitive sudoku

This archive contains template code for a competitive sudoku assignment. The goal of the assignment is to build an AI for a competitive sudoku player.

Contents

• The script 'simulate_game.py' is used for running a competitive sudoku game.

• The folder 'bin' contains a sudoku solver that is used by simulate_game.py.

• The folder 'boards' contains files with starting positions for a game.

• The folder 'competitive_sudoku' is a python module with basic functionality needed for running a sudoku game.

• The folders 'greedy_player', 'naive_player', 'random_player' and 'random_save_player' are four python modules with predefined sudoku AI's. All four of them play random moves.

  *) The naive_player does not check for duplicate entries in a region. *) The random_player checks for duplicate entries in a region. *) The greedy_player checks for duplicate entries in a region, and it does a 1 ply deep search to maximize the reward of a move. *) The random_save_player is a duplicate of random_player but using the save functionalities as defined in the SudokuAI base class

  Note that 'greedy_player', 'random_player' and 'random_save_player' make use of the sudoku solver. This is not allowed in the assignment.

Requirements

Python 3.8 or higher is required to run the code. No additional python packages need to be installed.

Running simulate_game.py

Some examples of running the script are:

simulate_game.py -h (print usage information)

simulate_game.py --check (check if the solver works; it should give output "The sudoku_solve program works.")

simulate_game.py (this will play a game between two random players on a board with 2x2 regions)

simulate_game.py --first=random_player --second=greedy_player --board=boards/empty-3x3.txt --time=1.0 (play a game between a random and a greedy player, starting on an empty board with 3x3 regions, and with 1 second per move)

File format

The file format for sudoku boards is as follows. A board with regions of size m x n (with m the number of rows and n the number of columns) is stored as the numbers m and n, followed by the (m * n) * m * n values of the squares of the board. Empty squares are printed as a dot '.'.

Below an example is given of a board with 2x3 regions.

2 3 . 3 . 1 5 2 . 1 . 4 . 3 5 . . . 1 4 1 . . 6 . . 3 2 1 . 4 6 . . . . . 1

Assignment code organization and constraints

Every team is assigned a number and every assignment has a code. Let's use '42' and 'A1' as examples; replace them as appropriate. Then the module name should be 'team42_A1', which is used as the folder name. The recommended way of doing this is to copy the folder 'naive_player' to the folder 'team42_A1'.

The team should create their own AI in this module. This is done replacing the 'compute_best_move' method of the 'SudokuAI' class in the 'sudokuai.py' file with your own implementation. This may involve adding other methods to the class, further functions to the module (in the 'sudokuai.py' file or further files).

Some constraints:

• The code must be written in python.

• The code must be single threaded.

• All code must be located inside the module folder of the team.

• If data files are used (for example data of a network) they must also be located inside the module folder of the team. See for example this page for an explanation on how to do that:

  https://dev.to/bowmanjd/easily-load-non-python-data-files-from-a-python-package-2e8g

• A requirement of a submission is that it should be possible to run it using simulate_game.py, without any modifications to this script, or to the code in the 'competitive_sudoku' folder. Test this!

• Transferring knowledge across moves is only possible through the save and load utility provided through the base class. This allows you to save any variable into a pickle file (.pkl) and load it back into the next move. Note that loading large amounts of data is costly.

Using python modules

If a command prompt is opened in the root folder of the archive, then the 'simulate_game.py' script should work out of the box. For other usages, it may be needed to add this root folder to the module search path, e.g. by adding this folder to the PYTHONPATH environment variable. See

https://docs.python.org/3/tutorial/modules.html

for an explanation about modules.