Here is a proposal to quantify the difficulty into 10 distinct levels using a hybrid approach of **Search Depth** (foresight) and **Error Rate** (probability of making a random move instead of the best move).
### Logic: The Hybrid Model
* **Depth (1-4):** Controls how far the AI calculates. Python is relatively slow, so Depth 4 is a realistic maximum for a responsive game without C++ extensions.
* **Error Rate (0.0 - 0.6):** The probability that the AI ignores the "best move" and picks a random legal move (simulating a blunder).
### Configuration Dictionary
| Level | Depth | Error Rate | Description |
| --- | --- | --- | --- |
| **1** | 1 | 60% | Calculates 1 move ahead, frequently plays randomly. |
| **2** | 1 | 40% | Still shortsighted, but slightly more coherent. |
| **3** | 1 | 20% | Rarely misses immediate captures, but has no plan. |
| **4** | 2 | 20% | Starts seeing 2 moves ahead, but makes mistakes. |
| **5** | 2 | 10% | Standard amateur level. Solid 2-move calculation. |
| **6** | 3 | 10% | Stronger. Sees 3 moves ahead, occasional errors. |
| **7** | 3 | 5% | Competent. Very few unforced errors. |
| **8** | 3 | 0% | Best possible play at Depth 3. |
| **9** | 4 | 5% | Deep calculation (slower), slight human-like noise. |
| **10** | 4 | 0% | Maximum strength. Pure Minimax at Depth 4. |
### Python Implementation
This code wraps your previous Minimax logic into a class structure that accepts a level (1-10).
```python
import chess
import math
import random
class ChessAI:
def __init__(self):
# Configuration for 10 levels
self.levels = {
1: {"depth": 1, "error_rate": 0.60},
2: {"depth": 1, "error_rate": 0.40},
3: {"depth": 1, "error_rate": 0.20},
4: {"depth": 2, "error_rate": 0.20},
5: {"depth": 2, "error_rate": 0.10},
6: {"depth": 3, "error_rate": 0.10},
7: {"depth": 3, "error_rate": 0.05},
8: {"depth": 3, "error_rate": 0.00},
9: {"depth": 4, "error_rate": 0.05},
10: {"depth": 4, "error_rate": 0.00},
}
# Piece values for evaluation
self.piece_values = {
chess.PAWN: 10, chess.KNIGHT: 30, chess.BISHOP: 30,
chess.ROOK: 50, chess.QUEEN: 90, chess.KING: 900
}
def get_move(self, board, level):
"""
Returns the best move based on the difficulty level (1-10).
"""
if level not in self.levels:
level = 5 # Default to medium
settings = self.levels[level]
depth = settings["depth"]
error_rate = settings["error_rate"]
# 1. Error Chance (Simulate Blunder)
# If the random roll is within the error rate, pick a random legal move.
if random.random() < error_rate:
return random.choice(list(board.legal_moves))
# 2. Strategic Calculation (Minimax)
# Otherwise, calculate the best move using Minimax.
return self._get_best_move_minimax(board, depth)
def _evaluate_board(self, board):
if board.is_checkmate():
return -9999 if board.turn else 9999
if board.is_stalemate() or board.is_insufficient_material():
return 0
score = 0
for square in chess.SQUARES:
piece = board.piece_at(square)
if piece:
value = self.piece_values[piece.piece_type]
if piece.color == chess.WHITE:
score += value
else:
score -= value
return score
def _minimax(self, board, depth, alpha, beta, maximizing):
if depth == 0 or board.is_game_over():
return self._evaluate_board(board)
legal_moves = list(board.legal_moves)
if maximizing:
max_eval = -math.inf
for move in legal_moves:
board.push(move)
eval = self._minimax(board, depth - 1, alpha, beta, False)
board.pop()
max_eval = max(max_eval, eval)
alpha = max(alpha, eval)
if beta <= alpha:
break
return max_eval
else:
min_eval = math.inf
for move in legal_moves:
board.push(move)
eval = self._minimax(board, depth - 1, alpha, beta, True)
board.pop()
min_eval = min(min_eval, eval)
beta = min(beta, eval)
if beta <= alpha:
break
return min_eval
def _get_best_move_minimax(self, board, depth):
best_move = None
best_value = -math.inf if board.turn == chess.WHITE else math.inf
alpha = -math.inf
beta = math.inf
legal_moves = list(board.legal_moves)
# Simple optimization: shuffle moves to prevent deterministic behavior on equal scores
random.shuffle(legal_moves)
for move in legal_moves:
board.push(move)
value = self._minimax(board, depth - 1, alpha, beta, not board.turn)
board.pop()
if board.turn == chess.WHITE:
if value > best_value:
best_value = value
best_move = move
alpha = max(alpha, best_value)
else:
if value < best_value:
best_value = value
best_move = move
beta = min(beta, best_value)
return best_move
# --- Usage Example ---
if __name__ == "__main__":
board = chess.Board()
ai = ChessAI()
# User selects Level 7
user_level = 7
print(f"AI Level: {user_level} (Depth: {ai.levels[user_level]['depth']}, Error: {ai.levels[user_level]['error_rate']*100}%)")
best_move = ai.get_move(board, level=user_level)
print(f"AI plays: {best_move}")
```
