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max2201111

maybe

Apr 12th, 2024
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Python 3.01 KB | Science | 0 0
  1. import chess
  2.  
  3. def simplify_fen_string(fen):
  4.     """Simplify the FEN string to just include the position of pieces without turn, castling, etc."""
  5.     parts = fen.split(' ')
  6.     simplified_fen = ' '.join(parts[:4])
  7.     return simplified_fen
  8.  
  9. def evaluate_position(board):
  10.     """Evaluate the board position to determine if it's a checkmate, stalemate, or ongoing game."""
  11.     if board.is_checkmate():
  12.         if board.turn == chess.WHITE:
  13.             return -1000  # White is checkmated, black wins
  14.         else:
  15.             return 1000   # Black is checkmated, white wins
  16.     elif board.is_stalemate() or board.is_insufficient_material() or board.can_claim_draw():
  17.         return 0  # Game is a draw
  18.     return None  # Game is still ongoing
  19.  
  20. def create_AR_entry(fen, result, parent):
  21.     """Create an entry for the analysis record (AR)."""
  22.     return {
  23.         'fen': fen,
  24.         'parent': parent,
  25.         'children': [],
  26.         'result': result,
  27.         'sequence': []
  28.     }
  29.  
  30. def generate_positions(board, AR, parent_fen):
  31.     """Generate all possible game positions from the current board state."""
  32.     current_fen = board.fen()
  33.     if len(AR) % 100000 == 0:
  34.         print(len(AR))
  35.  
  36.     if current_fen in AR:
  37.         return  # Avoid processing the same position twice
  38.  
  39.     result = evaluate_position(board)
  40.     AR[current_fen] = create_AR_entry(simplify_fen_string(current_fen), result, parent_fen)
  41.  
  42.     if parent_fen:
  43.         AR[parent_fen]['children'].append(current_fen)
  44.  
  45.     if result is not None:
  46.         return  # Stop further generation if the game has ended
  47.  
  48.     for move in board.legal_moves:
  49.         board.push(move)
  50.         generate_positions(board, AR, current_fen)
  51.         board.pop()
  52.  
  53. def propagate_upwards(AR, child_fen):
  54.     """Recursively update parent nodes based on the results of child nodes."""
  55.     child_node = AR[child_fen]
  56.     parent_fen = child_node['parent']
  57.  
  58.     while parent_fen:
  59.         parent_node = AR[parent_fen]
  60.         if parent_node['result'] is None or abs(child_node['result']) < abs(parent_node['result']):
  61.             parent_node['result'] = -child_node['result']
  62.             parent_node['sequence'] = [child_fen] + child_node['sequence']
  63.         child_fen = parent_fen
  64.         child_node = parent_node
  65.         parent_fen = child_node['parent']
  66.  
  67. def back_propagation(AR):
  68.     """Propagate the results from the leaf nodes to the root based on the analysis record."""
  69.     for fen in AR:
  70.         node = AR[fen]
  71.         if node['result'] is not None and node['parent']:
  72.             propagate_upwards(AR, fen)
  73.  
  74. def main():
  75.     initial_fen = "8/8/8/8/3Q4/5K2/8/4k3 w - - 0 1"
  76.     board = chess.Board(initial_fen)
  77.     AR = {}
  78.     generate_positions(board, AR, None)
  79.     back_propagation(AR)
  80.  
  81.     # Output results
  82.     initial_entry = AR[initial_fen]
  83.     print(f"Result for initial position: {initial_entry['result']}")
  84.     print("Optimal sequence of moves:")
  85.     for fen in initial_entry['sequence']:
  86.         print(chess.Board(fen))
  87.         print()
  88.  
  89. main()
  90.  
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