def openingStaticImproved(position):
""" improve the static estimation by adding the number of potential mills and number of double mills """
piecesDiff = MorrisGame.openingStatic(position)
num2PiecesConf = count2PiecesConf(position)
if num2PiecesConf >= 2:
num3PiecesConf = num2PiecesConf - 1
else:
num3PiecesConf = 0
# staticEs = 6 * piecesDiff + 12 * num2PiecesConf + 7 * num3PiecesConf + 26 * countMills(position)
# staticEs = piecesDiff + 6 * num2PiecesConf
staticEs = piecesDiff + 2 * num2PiecesConf + 10 * num3PiecesConf
return staticEs
def MinMaxOpening(board, depth):
""" use MINIMAX algorithm to choose the move for 'MIN' """
if depth == 0:
board.value = MorrisGame.openingStatic(board.position)
MorrisGame.numEvaluate += 1
return board
else:
board.child = MorrisGame.genMoveOpeningBlack(board.position)
minValue = float('inf')
retBoard = None
for child in board.child:
result = MaxMinOpening(child, depth - 1)
if minValue > result.value:
retBoard = result # for test
minValue = result.value
retBoard = child
retBoard.value = minValue
return retBoard
def MinMaxABOpening(board, depth, alpha, beta):
""" use Alpha-Beta pruning to choose the move for 'MIN' """
if depth == 0:
board.value = MorrisGame.openingStatic(board.position)
MorrisGame.numEvaluate += 1
return board
else:
board.child = MorrisGame.genMoveOpeningBlack(board.position)
minValue = float('inf')
retBoard = None
for child in board.child:
result = MaxMinABOpening(child, depth - 1, alpha, beta)
if minValue > result.value:
minValue = result.value
retBoard = child
retBoard.value = minValue
if minValue <= alpha:
return retBoard
elif minValue < beta:
beta = minValue
return retBoard
def MaxMinABOpening(board, depth, alpha, beta):
""" use Alpha-Beta pruning to choose the move for 'MAX' """
if depth == 0:
board.value = MorrisGame.openingStatic(board.position)
MorrisGame.numEvaluate += 1
return board
else:
board.child = MorrisGame.genMoveOpening(board.position)
maxValue = float('-inf')
retBoard = None
for child in board.child:
result = MinMaxABOpening(child, depth - 1, alpha, beta)
if maxValue < result.value:
maxValue = result.value
retBoard = child
retBoard.value = maxValue
if maxValue >= beta:
return retBoard
else:
if maxValue > alpha:
alpha = maxValue
return retBoard
