Implement QSEE pruning
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@ -187,37 +187,75 @@ proc getEstimatedMoveScore(self: SearchManager, move: Move, ply: int): int =
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if move.isTactical():
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let seeScore = self.board.position.see(move)
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# We want to prioritize good captures (see > 0), but if the capture
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# is bad then at least we sort it with MVVLVA
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if seeScore < 0 and (move.isCapture() or move.isEnPassant()):
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# Implementation of MVVLVA: Most Valuable Victim Least Valuable Aggressor.
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# We prioritize moves that capture the most valuable pieces, and as a
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# second goal we want to use our least valuable pieces to do so (this
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# is why we multiply the score of the captured piece by a constant, to give
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# it priority)
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let capturedScore = MVV_LVA_MULTIPLIER * self.board.position.getPieceScore(move.targetSquare)
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result = capturedScore - self.board.position.getPieceScore(move.startSquare)
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# If the capture is also a promotion we want to give it an even bigger bonus
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if move.isPromotion():
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var piece: Piece
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case move.getPromotionType():
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of PromoteToBishop:
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piece = Piece(kind: Bishop, color: sideToMove)
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of PromoteToKnight:
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piece = Piece(kind: Knight, color: sideToMove)
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of PromoteToRook:
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piece = Piece(kind: Rook, color: sideToMove)
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of PromoteToQueen:
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piece = Piece(kind: Queen, color: sideToMove)
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else:
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discard # Unreachable
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result += PROMOTION_MULTIPLIER * self.board.position.getPieceScore(piece, move.targetSquare)
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when not defined(SEE2):
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# We want to prioritize good captures (see > 0), but if the capture
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# is bad then at least we sort it with MVVLVA
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if seeScore < 0 and (move.isCapture() or move.isEnPassant()):
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# Implementation of MVVLVA: Most Valuable Victim Least Valuable Aggressor.
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# We prioritize moves that capture the most valuable pieces, and as a
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# second goal we want to use our least valuable pieces to do so (this
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# is why we multiply the score of the captured piece by a constant, to give
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# it priority)
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let capturedScore = MVV_LVA_MULTIPLIER * self.board.position.getPieceScore(move.targetSquare)
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result = capturedScore - self.board.position.getPieceScore(move.startSquare)
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# If the capture is also a promotion we want to give it an even bigger bonus
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if move.isPromotion():
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var piece: Piece
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case move.getPromotionType():
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of PromoteToBishop:
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piece = Piece(kind: Bishop, color: sideToMove)
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of PromoteToKnight:
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piece = Piece(kind: Knight, color: sideToMove)
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of PromoteToRook:
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piece = Piece(kind: Rook, color: sideToMove)
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of PromoteToQueen:
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piece = Piece(kind: Queen, color: sideToMove)
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else:
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discard # Unreachable
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result += PROMOTION_MULTIPLIER * self.board.position.getPieceScore(piece, move.targetSquare)
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return result + BAD_SEE_OFFSET
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return result + BAD_SEE_OFFSET
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else:
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# If the capture is good then we just use the SEE score + the offset
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return seeScore + GOOD_SEE_OFFSET
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else:
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# If the capture is good then we just use the SEE score + the offset
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return seeScore + GOOD_SEE_OFFSET
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# We want to prioritize good captures (see > 0) and then sort
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# them with MVVLVA. Of course, good captures will be placed
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# before bad captures regardless of what MVVLVA has to say
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# about them
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if move.isCapture() or move.isEnPassant():
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# Implementation of MVVLVA: Most Valuable Victim Least Valuable Aggressor.
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# We prioritize moves that capture the most valuable pieces, and as a
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# second goal we want to use our least valuable pieces to do so (this
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# is why we multiply the score of the captured piece by a constant, to give
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# it priority)
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let capturedScore = MVV_LVA_MULTIPLIER * self.board.position.getPieceScore(move.targetSquare)
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result = capturedScore - self.board.position.getPieceScore(move.startSquare)
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# If the capture is also a promotion we want to give it an even bigger bonus
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if move.isPromotion():
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var piece: Piece
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case move.getPromotionType():
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of PromoteToBishop:
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piece = Piece(kind: Bishop, color: sideToMove)
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of PromoteToKnight:
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piece = Piece(kind: Knight, color: sideToMove)
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of PromoteToRook:
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piece = Piece(kind: Rook, color: sideToMove)
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of PromoteToQueen:
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piece = Piece(kind: Queen, color: sideToMove)
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else:
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discard # Unreachable
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result += PROMOTION_MULTIPLIER * self.board.position.getPieceScore(piece, move.targetSquare)
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result += seeScore
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# We use >= instead of > because we're evaluating promotions by
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# their SEE scores as well, which would move them all to the back
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# in cases where a promotion ends up with no material loss
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if seeScore >= 0:
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return result + GOOD_SEE_OFFSET
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else:
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return result + BAD_SEE_OFFSET
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if move.isQuiet():
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# History heuristic bonus
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@ -341,6 +379,9 @@ proc qsearch(self: var SearchManager, ply: int, alpha, beta: Score): Score =
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var bestScore = score
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var alpha = max(alpha, score)
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for move in moves:
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# Skip bad captures (gains 52.9 +/- 25.2)
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if self.board.position.see(move) < 0:
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continue
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self.board.doMove(move)
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inc(self.nodeCount)
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let score = -self.qsearch(ply + 1, -beta, -alpha)
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