Скачать или смотреть AI Checkers using Minimax Algorithm.

The following assignment was designed as part of Florida International University's CAP 4630 Artificial Intelligence course.

In this assignment, students will be able to develop an AI Checkers application using minimax algorithm as shown in this video.

The first step is to download the template here:
https://drive.google.com/file/d/1VsLH...
All simulations were done using Python 3.8 and pygame package.
To install pygame in Windows: https://www.geeksforgeeks.org/how-to-...
To install pygame in MacOS: https://www.geeksforgeeks.org/install...
To install pygame in Linux: https://www.geeksforgeeks.org/install...

The zip file contains 3 folders (assets, checkers, minimax) and the .py file main.py

assets folder: contains resources for the game
checkers folder: contains the following .py files
board.py = the state of the board and board graphics
constants.py = defines constant parameters for the game
game.py = defines the rules of the game
piece.py = defines piece drawing instructions and moving rules
minimax folder: contains the following .py file
algorithm.py = contains the minimax algorithm. This is the file you should modify.
main.py = file needed to run the simulation.


Part 1) In this part, students need to complete the minimax algorithm in the algorithm.py file. The current function is defined as

def minimax(position, depth, max_player, game):

if max_player:
This part is given.
return maxEval, best_move

else:
This is the part to be added.
return minEval, best_move

where
position is a board object. This is the current board configuration. Given the current board configuration, the minimax should provide the optimal board configuration (best board configuration) as we play.

depth represents how far we are extending the minimax tree. Every time we evaluate the minimax algorithm, we will decrease the depth by 1. Remember: we only evaluate a position when we reach the end of the tree (root node).
max_player is a boolean value (True/False) that selects the player (max_player, if we are maximizing or min_player if we are minimizing the reward.

game is an object defined in main.py and is used to draw and update the board as we play.

Given the max_player script, add the min_player part to complete the minimax algorithm. To check your solution, run main.py

Part 2) Now that the algorithm is completed and running as shown in the video, change the depth parameter of the minimax tree main.py to 3 and then to 4, and discuss your findings.

Note: if you want to check the decision-making process of the minimax algorithm, you can uncomment draw_moves(game, board, piece) in algorithm.py.

Ref: Tech with Tim