The Min-Max Algorithm is a decision-making algorithm used in game theory, particularly in two-player, zero-sum games. It is typically used in adversarial search, where two players take turns making moves, and each player's goal is to either maximize their chances of winning (for one player) or minimize the chances of the opponent winning (for the other player). The algorithm evaluates all possible moves by simulating each of the moves to their fullest extent, assuming both players play optimally. In this Python program, we will implement the Min-Max Algorithm for a simple game of Tic-Tac-Toe, where the computer will make its moves using the Min-Max algorithm to maximize its chances of winning, while the user will make their moves manually. The game assumes a player plays 'X' and the computer plays 'O'. The board is represented by a 3x3 matrix, and the computer will select the best possible move using the Min-Max strategy. Here is a Python implementation of the Min-Max Algorithm for Tic-Tac-Toe: ```python import math # Constants to represent the players PLAYER_X = 'X' PLAYER_O = 'O' EMPTY = ' ' # Function to check if the current player has won def check_winner(board, player): # Check rows, columns, and diagonals for i in range(3): if all([cell == player for cell in board[i]]): # Check row return True if all([board[j][i] == player for j in range(3)]): # Check column return True if all([board[i][i] == player for i in range(3)]): # Check diagonal return True if all([board[i][2 - i] == player for i in range(3)]): # Check other diagonal return True return False # Function to check if the board is full def is_full(board): return all([cell != EMPTY for row in