The Magic of Chess AI: Classical Algorithms

Chess is an intricate dance of strategy, and teaching a machine to master this dance is no small feat. This first part of our chess AI journey delves into classical algorithms that set the foundation.

The Digital Thought Process:

When facing an opponent, your mind races through potential moves. Similarly, an AI evaluates the chess board systematically, simulating various moves and their outcomes.

Peeking into the Future with Minimax:

Minimax is the AI's way of simulating the game several moves ahead. It tries out various move combinations for both sides and assesses potential outcomes.

```
def minimax(position, depth, is_maximizing):
    if depth == 0:
        return evaluate_board(position)

    possible_moves = generate_moves(position)
    
    if is_maximizing:
        max_eval = float('-inf')
        for move in possible_moves:
            eval = minimax(move, depth-1, False)
            max_eval = max(max_eval, eval)
        return max_eval
    else:
        min_eval = float('inf')
        for move in possible_moves:
            eval = minimax(move, depth-1, True)
            min_eval = min(min_eval, eval)
        return min_eval
```

Evaluating the Board:

The AI uses an evaluation function to get a "feel" of the board. This might be a simple piece count or a more intricate assessment of the board's state.

```
def evaluate_board(position):
    piece_values = {'P': 1, 'N': 3, 'B': 3, 'R': 5, 'Q': 9, 'K': 100}
    evaluation = 0
    for row in position:
        for piece in row:
            if piece in piece_values:
                evaluation += piece_values[piece]
            elif piece.lower() in piece_values:
                evaluation -= piece_values[piece.lower()]
    return evaluation
```

Speeding Things Up: Alpha-Beta Pruning

To optimize its thought process, the AI uses Alpha-Beta pruning to ignore unpromising moves, akin to a chess grandmaster.

```
def minimax_with_alpha_beta(position, depth, alpha, beta, is_maximizing):
    if depth == 0:
        return evaluate_board(position)

    possible_moves = generate_moves(position)
    
    if is_maximizing:
        max_eval = float('-inf')
        for move in possible_moves:
            eval = minimax_with_alpha_beta(move, depth-1, alpha, beta, False)
            max_eval = max(max_eval, eval)
            alpha = max(alpha, eval)
            if beta <= alpha:
                break
        return max_eval
    else:
        min_eval = float('inf')
        for move in possible_moves:
            eval = minimax_with_alpha_beta(move, depth-1, alpha, beta, True)
            min_eval = min(min_eval, eval)
            beta = min(beta, eval)
            if beta <= alpha:
                break
        return min_eval
```

Classical algorithms give our chess AI the foundational logic it needs to evaluate and decide moves. Stay tuned for the next blog, where we explore the modern magic of deep learning in chess!