Week 3 content of chess bot
Chess Bot Week 3 - Advanced Evaluation Functions
Last week, we implemented the minimax algorithm with a basic material-only evaluation function. This week, we'll dive deep into creating sophisticated evaluation functions that will help your bot understand chess positions like a human player.
Project Overview
This week, we'll expand our evaluation function to consider multiple aspects of chess strategy. Your bot will learn to evaluate:
- Advanced material counting
- Piece positioning and mobility
- Pawn structure analysis
- King safety
- Control of key squares
- Development and tempo
Understanding Position Evaluation
Core Concept
An evaluation function converts a chess position into a numerical score. Positive scores favor White, negative scores favor Black. The better the position for White, the higher the score.
Components of a Strong Evaluation Function
1. Material Count
- Foundation of most evaluation functions
- Standard piece values are approximate:
- Pawn = 100
- Knight = 320
- Bishop = 330
- Rook = 500
- Queen = 900
- King = 20000 (effectively infinite)
- Consider piece pairs:
- Bishop pair bonus (+50)
- Rook pair penalty (-10)
- Adjust values based on game phase:
- Knights better in closed positions
- Bishops better in open positions
- Rooks stronger in endgame
2. Piece Positioning
- Use piece-square tables
- Different tables for middlegame and endgame
- Example pawn positioning values:
```
// Middlegame pawn position values
const PAWN_TABLE_MG = [
0, 0, 0, 0, 0, 0, 0, 0,
50, 50, 50, 50, 50, 50, 50, 50,
10, 10, 20, 30, 30, 20, 10, 10,
5, 5, 10, 25, 25, 10, 5, 5,
0, 0, 0, 20, 20, 0, 0, 0,
5, -5,-10, 0, 0,-10, -5, 5,
5, 10, 10,-20,-20, 10, 10, 5,
0, 0, 0, 0, 0, 0, 0, 0
];
```
3. King Safety
- Pawn shield evaluation
- Open files near king (-20 per file)
- Attack patterns recognition
- Tropism (distance of enemy pieces to king)
- Different evaluation for middlegame vs endgame
4. Pawn Structure
- Isolated pawns (-20)
- Doubled pawns (-10)
- Backward pawns (-15)
- Passed pawns (+20, bonus increases with rank)
- Connected passed pawns (+40)
- Pawn chains
- Pawn islands
Basic Implementation:
```python
class ChessBot:
def evaluate_position(self, board):
if board.is_game_over():
if board.is_checkmate():
return -10000 if board.turn else 10000
return 0 # Draw
score = 0
# Material and piece position evaluation
score += self.evaluate_material(board)
score += self.evaluate_piece_position(board)
# Pawn structure
score += self.evaluate_pawn_structure(board)
# King safety
score += self.evaluate_king_safety(board)
# Mobility
score += self.evaluate_mobility(board)
return score
def evaluate_material(self, board):
score = 0
piece_values = {
'P': 100, 'N': 320, 'B': 330,
'R': 500, 'Q': 900, 'K': 20000
}
# Basic material count
for piece in piece_values:
score += len(board.pieces(piece, True)) * piece_values[piece]
score -= len(board.pieces(piece, False)) * piece_values[piece]
# Bishop pair bonus
if len(board.pieces(chess.BISHOP, True)) >= 2:
score += 50
if len(board.pieces(chess.BISHOP, False)) >= 2:
score -= 50
return score
def evaluate_pawn_structure(self, board):
score = 0
# Evaluate for both colors
for color in [True, False]:
multiplier = 1 if color else -1
pawns = board.pieces(chess.PAWN, color)
# Check each file for isolated pawns
for file in range(8):
pawns_in_file = sum(1 for pawn in pawns
if chess.square_file(pawn) == file)
if pawns_in_file > 0:
# Check adjacent files
adjacent_pawns = sum(1 for pawn in pawns
if chess.square_file(pawn) in [file-1, file+1])
if adjacent_pawns == 0:
score -= 20 * multiplier # Isolated pawn penalty
if pawns_in_file > 1:
score -= 10 * multiplier # Doubled pawn penalty
return score
def evaluate_king_safety(self, board):
score = 0
# Evaluate pawn shield for both kings
for color in [True, False]:
multiplier = 1 if color else -1
king_square = board.king(color)
if king_square is None:
continue
king_file = chess.square_file(king_square)
king_rank = chess.square_rank(king_square)
# Check pawn shield
shield_score = 0
for file in range(max(0, king_file - 1), min(8, king_file + 2)):
shield_rank = king_rank + (1 if color else -1)
shield_square = chess.square(file, shield_rank)
if board.piece_at(shield_square) == chess.Piece(chess.PAWN, color):
shield_score += 10
score += shield_score * multiplier
return score
```
Understanding the Code
1. Main Evaluation Function
- Combines scores from multiple evaluation components
- Each component can be tuned independently
- Weights can be adjusted based on playing strength
2. Material Evaluation
- Basic piece counting with standard values
- Additional bonuses for piece combinations
- Can be extended with phase-dependent values
3. Pawn Structure Analysis
- Checks for common pawn weaknesses
- Evaluates pawn chains and islands
- Identifies passed pawns and their value
4. King Safety Evaluation
- Analyzes pawn shield
- Checks for open files near king
- Can be extended with attack pattern recognition
Advanced Concepts
1. Tapered Evaluation
```python
def get_game_phase(board):
"""
Returns a value between 0 (endgame) and 256 (opening)
based on remaining material
"""
npm = 0 # Non-pawn material
for piece_type in [chess.KNIGHT, chess.BISHOP, chess.ROOK, chess.QUEEN]:
npm += len(board.pieces(piece_type, True)) * PIECE_VALUES[piece_type]
npm += len(board.pieces(piece_type, False)) * PIECE_VALUES[piece_type]
return min(npm, 256)
def interpolate(mg_score, eg_score, phase):
"""
Interpolate between middlegame and endgame scores
based on game phase
"""
return ((mg_score phase) + (eg_score (256 - phase))) // 256
```
2. Mobility Evaluation
- Count legal moves for each piece
- Weight moves based on piece type and position
- Consider control of key squares
3. Pattern Recognition
- Identify common tactical patterns
- Evaluate piece coordination
- Recognize strategic themes
What to Get Done This Week
1. Implement the basic evaluation components:
- Material counting with piece pairs
- Simple pawn structure evaluation
- Basic king safety
2. Create piece-square tables for:
- Pawns
- Knights
- Bishops
- Rooks
- Queens
- Kings (different for middlegame/endgame)
3. Add tapered evaluation:
- Implement game phase detection
- Create separate middlegame/endgame scores
- Interpolate based on remaining material
4. Test and tune your evaluation:
- Play test games
- Analyze positions where evaluation seems wrong
- Adjust weights and bonuses
5. Optional advanced features:
- Mobility evaluation
- Pattern recognition
- Tactical awareness
Additional Resources:
* [Chess Programming Wiki - Evaluation](https://www.chessprogramming.org/Evaluation)
* [Piece-Square Tables](https://www.chessprogramming.org/Piece-Square_Tables)
* [Pawn Structure](https://www.chessprogramming.org/Pawn_Structure)
Remember that evaluation functions require extensive testing and tuning. Keep track of positions where your bot makes mistakes and use them to improve your evaluation function.
Next week, we'll look at advanced search techniques including quiescence search and late move reductions. Good luck with your implementation!
