Description

The Minimax Algorithm is a decision-making algorithm used in two-player games (like Tic-Tac-Toe, Chess, or Checkers). It assumes both players play optimally:

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• One player (Max) tries to maximize their score.
• The other player (Min) tries to minimize the opponent’s score.

The algorithm evaluates possible moves by exploring the game tree and choosing the move that leads to the best possible outcome assuming the opponent is also playing optimally.

Examples (Code)

Below is a simple example of AI using python to create a basic decision-making system:

# Minimax algorithm for Tic-Tac-Toe
def minimax(board, depth, is_maximizing):
    score = evaluate(board)
    
    if score == 10 or score == -10:
        return score
    
    if is_draw(board):
        return 0

    if is_maximizing:
        best = -float('inf')
        for move in get_available_moves(board):
            make_move(board, move, 'X')
            best = max(best, minimax(board, depth + 1, False))
            undo_move(board, move)
        return best
    else:
        best = float('inf')
        for move in get_available_moves(board):
            make_move(board, move, 'O')
            best = min(best, minimax(board, depth + 1, True))
            undo_move(board, move)
        return best

Real-World Applications

Where the Topic Is Applied

Resources

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