Week 4 content of chess bot
Last week, we enhanced our evaluation function with sophisticated position assessment techniques. This week, we'll focus on a critical aspect of chess engines: the opening phase of the game. By implementing an opening book, your bot will start games with strong, proven moves rather than relying solely on search algorithms for early decisions.
This week, we'll implement opening books to enable your bot to:
- Play established opening sequences
- Avoid calculation-heavy early positions
- Develop a varied and unpredictable playing style
- Build favorable middle-game positions
- Save computation time for more complex positions
An opening book is a collection of pre-analyzed chess positions and their corresponding best moves. Instead of calculating moves in well-known positions, chess engines can simply look up the best move from their opening book, which:
- Saves computational resources
- Leads to stronger play based on human grandmaster experience
- Avoids common opening traps and mistakes
1. Polyglot Books
- Industry standard binary format
- Compact representation of positions and moves
- Used by many chess engines including Stockfish
- Easy to integrate with most chess libraries
2. PGN Databases
- Collection of games in Portable Game Notation
- Can be filtered for high-rated players or specific openings
- Requires parsing and tree-building logic
3. Custom Opening Trees
- Tailored to specific playing styles
- Can include probability weights for move selection
- Allows for multiple responses to the same position
class OpeningBook:
def init(self, book_path=None):
self.book = {} # Dictionary: FEN -> list of (move, weight) tuples
if book_path:
self.load_book(book_path)
def load_book(self, book_path):
"""Load opening book from file"""
# Polyglot book loading implementation
try:
with open(book_path, "rb") as book_file:
while True:
entry_data = book_file.read(16)
if len(entry_data) < 16:
break
key = int.from_bytes(entry_data[0:8], byteorder="big")
move = int.from_bytes(entry_data[8:10], byteorder="big")
weight = int.from_bytes(entry_data[10:12], byteorder="big")
learn = int.from_bytes(entry_data[12:16], byteorder="big")
# Convert key to FEN, move to UCI notation
fen = self._key_to_fen(key) # Simplified, actual implementation is complex
uci_move = self._bin_to_uci(move)
self.book[fen].append((uci_move, weight))
else:
self.book[fen] = [(uci_move, weight)]
except Exception as e:
print(f"Error loading opening book: {e}")
def get_move(self, board, variation=0.1):
"""Get a move from the opening book for the current position"""
try:
# Get simplified FEN (only piece positions and turn)
fen = self._simplified_fen(board)
# Sort by weight, highest first
moves.sort(key=lambda x: x[1], reverse=True)
# Introduce variation if requested
if variation > 0 and len(moves) > 1:
# Sometimes pick not the best move but a random good one
if random.random() < variation:
# Pick from top 3 moves with probability proportional to weight
top_moves = moves[:min(3, len(moves))]
total_weight = sum(weight for , weight in topmoves)
r = random.random() * total_weight
cumulative = 0
for move, weight in top_moves:
cumulative += weight
if r <= cumulative:
return move
# Default: return highest weighted move
return moves[0][0]
return None # No book move available
except Exception as e:
print(f"Error getting book move: {e}")
return None
def simplifiedfen(self, board):
"""Return simplified FEN (position and turn only)"""
full_fen = board.fen()
return ' '.join(full_fen.split(' ')[:2])
```
```python
class ChessBot:
def init(self, book_path=None):
self.book = OpeningBook(book_path)
self.transposition_table = {}
self.history_table = {}
# ... other initializations
def choose_move(self, board, depth=4):
# First check if we have a book move
book_move = self.book.get_move(board)
if book_move:
# Verify the move is legal
try:
move = chess.Move.from_uci(book_move)
if move in board.legal_moves:
return move
except ValueError:
pass # Invalid UCI format, ignore
# No book move, use search algorithm
return self.minimax(board, depth)
# ... rest of your bot implementation
```
If you want to create a basic custom opening book without using external libraries:
```python
def create_simple_book():
book = {}
# Starting position
starting_fen = chess.STARTING_FEN.split(' ')[0] + ' w'
book[starting_fen] = [
('e2e4', 100), # King's Pawn Opening
('d2d4', 90), # Queen's Pawn Opening
('c2c4', 80), # English Opening
('g1f3', 70) # Réti Opening
]
# After 1. e4
board = chess.Board()
board.push_san("e4")
fen = board.fen().split(' ')[0] + ' b'
book[fen] = [
('e7e5', 100), # Open Game
('c7c5', 90), # Sicilian Defense
('e7e6', 80), # French Defense
('c7c6', 70) # Caro-Kann Defense
]
# After 1. d4
board = chess.Board()
board.push_san("d4")
fen = board.fen().split(' ')[0] + ' b'
book[fen] = [
('d7d5', 100), # Closed Game
('g8f6', 90), # Indian Defense
('f7f5', 80), # Dutch Defense
('e7e6', 70) # Queen's Pawn Game
]
# ... add more positions as needed
return book
```
The Polyglot format uses Zobrist hashing to create unique keys for positions:
```python
def calculate_zobrist_hash(board):
"""Calculate Zobrist hash for a chess position"""
hash_val = 0
# XOR piece positions
for square in chess.SQUARES:
piece = board.piece_at(square)
if piece:
piece_idx = (piece.piece_type - 1) * 2 + (0 if piece.color else 1)
square_idx = 63 - square # Polyglot uses a1=0, h8=63 indexing
hash_val ^= ZOBRIST_PIECE_KEYS[piece_idx][square_idx]
# XOR castling rights
if board.has_kingside_castling_rights(chess.WHITE):
hash_val ^= ZOBRIST_CASTLING_KEYS[0]
if board.has_queenside_castling_rights(chess.WHITE):
hash_val ^= ZOBRIST_CASTLING_KEYS[1]
if board.has_kingside_castling_rights(chess.BLACK):
hash_val ^= ZOBRIST_CASTLING_KEYS[2]
if board.has_queenside_castling_rights(chess.BLACK):
hash_val ^= ZOBRIST_CASTLING_KEYS[3]
# XOR en passant
if board.ep_square:
file = chess.square_file(board.ep_square)
hash_val ^= ZOBRIST_EP_KEYS[file]
# XOR turn
if board.turn == chess.BLACK:
hash_val ^= ZOBRIST_TURN_KEY
return hash_val
```
```python
def read_polyglot_entry(data):
"""Parse a 16-byte polyglot entry"""
key = int.from_bytes(data[0:8], byteorder="big")
raw_move = int.from_bytes(data[8:10], byteorder="big")
weight = int.from_bytes(data[10:12], byteorder="big")
learn = int.from_bytes(data[12:16], byteorder="big")
# Decode move
from_file = (raw_move >> 6) & 7
from_rank = (raw_move >> 9) & 7
to_file = (raw_move >> 0) & 7
to_rank = (raw_move >> 3) & 7
promotion = (raw_move >> 12) & 7
# Convert to UCI format
from_square = chess.square(from_file, from_rank)
to_square = chess.square(to_file, to_rank)
move = chess.Move(from_square, to_square)
# Handle promotion
if promotion:
promotion_pieces = [None, chess.KNIGHT, chess.BISHOP, chess.ROOK, chess.QUEEN]
move.promotion = promotion_pieces[promotion]
return key, move, weight, learn
```
Here are some common opening lines that you might want to include in your custom book:
1. King's Pawn Openings
- Ruy López: 1.e4 e5 2.Nf3 Nc6 3.Bb5
- Italian Game: 1.e4 e5 2.Nf3 Nc6 3.Bc4
- Sicilian Defense: 1.e4 c5
- French Defense: 1.e4 e6
2. Queen's Pawn Openings
- Queen's Gambit: 1.d4 d5 2.c4
- Indian Defenses: 1.d4 Nf6
- Dutch Defense: 1.d4 f5
3. Flank Openings
- English Opening: 1.c4
- Réti Opening: 1.Nf3 d5 2.c4
1. Book Width vs Depth
- Width: more variety in openings
- Depth: deeper knowledge of fewer openings
- Consider your computational resources
2. Randomization
- Add variation by selecting non-top moves occasionally
- Weighted randomization based on move strength
- Avoid predictable play patterns
3. Opening Selection
- Universal book vs specialized repertoire
- Opponent-specific preparation
- Tournament vs casual play considerations
```python
def update_opening_weights(book, game_result, player_color):
"""Update opening book weights based on game results"""
# Extract moves played from book
board = chess.Board()
moves_played = []
for move in game.mainline_moves():
fen = board.fen().split(' ')[0] + (' w' if board.turn else ' b')
if fen in book:
uci = move.uci()
for i, (book_move, weight) in enumerate(book[fen]):
if book_move == uci:
moves_played.append((fen, i))
break
board.push(move)
if len(moves_played) == 0:
break # Left book
# Adjust weights based on result
result_modifier = 0
if game_result == '1-0':
result_modifier = 1 if player_color else -1
elif game_result == '0-1':
result_modifier = -1 if player_color else 1
# Update weights
for fen, move_index in moves_played:
book[fen][move_index] = (
book[fen][move_index][0],
max(1, book[fen][move_index][1] + result_modifier * 5)
)
```
Using Python-Chess with Polyglot:
```python
import chess.polyglot
def get_book_move(board, book_path):
try:
with chess.polyglot.open_reader(book_path) as reader:
entries = list(reader.find_all(board))
if entries:
# Sort by weight and pick the best
entries.sort(key=lambda entry: entry.weight, reverse=True)
return entries[0].move
except:
pass
return None
```
1. Set up a basic opening book system:
- Create a simple dictionary-based book for common openings
- Implement book move lookups in your bot
2. Integrate with standard book formats:
- Add support for Polyglot (.bin) books
- Test with existing opening books
3. Add book selection strategies:
- Implement weighted random selection
- Add a "book depth" parameter to control when to leave book play
4. Test your opening book:
- Play test games starting from various positions
- Compare performance with and without the opening book
5. Optional advanced features:
- Opening learning from played games
- Tournament-specific book preparation
- User-selectable opening styles
* [Chess Programming Wiki - Opening Book](https://www.chessprogramming.org/Opening_Book)
* [Polyglot Book Format](https://www.chessprogramming.org/Polyglot)
* [Free Chess Opening Books](https://sourceforge.net/projects/codekiddy-chess/files/Books/Polyglot/)
* [Common Chess Openings](https://www.chess.com/openings)
Remember that a good opening book balances between breadth of coverage and depth of analysis. Start with a small, focused book and gradually expand it as your bot becomes stronger.
Next week, we'll explore advanced search techniques like quiescence search, null move pruning, and late move reductions to make your bot even stronger. Good luck with your implementation!
