def evaluation(board, color):
global myColor
color = myColor
global moveCount
opponentColor = gamePlay.getOpponentColor(color)
value = 0
global fourth_layer
# Loop through all board positions
for piece in range(1, 33):
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
# Eval on opponent pieces <= 7 or for first 10 moves(Be in attacking mode)
if board[x][y].upper() == color.upper() and (
countPieces(board, opponentColor) <= 7 or moveCount <= 10):
value = value + 1
continue
elif board[x][y].upper() == opponentColor.upper() and (
countPieces(board, opponentColor) <= 7 or moveCount <= 10):
value = value - 1
continue
# Eval on pieces in fourth layer(pieces will tend to move towards 4th outer most layer ie defensive positions)
if board[x][y].upper() == color.upper() and piece in fourth_layer:
value = value + 4
continue
elif board[x][y].upper() == opponentColor.upper(
) and piece in fourth_layer:
value = value - 4
continue
# Eval on pieces in third layer (pieces will tend to move towards 3rd layer on board)
if board[x][y].upper() == color.upper() and piece in third_layer:
value = value + 3
continue
elif board[x][y].upper() == opponentColor.upper(
) and piece in third_layer:
value = value - 3
continue
# Eval on pieces in second layer (pieces will tend to move towards 2nd layer on board
if board[x][y].upper() == color.upper() and piece in second_layer:
value = value + 2
continue
elif board[x][y].upper() == opponentColor.upper(
) and piece in second_layer:
value = value - 2
continue
# Eval on pieces in first layer (pieces will tend to move to center two pieces)
if board[x][y].upper() == color.upper() and piece in first_layer:
value = value + 1
continue
elif board[x][y].upper() == opponentColor.upper(
) and piece in first_layer:
value = value - 1
continue
return value
def minimax(board,depth,alpha,beta,maximizingPlayer):
global currentPlayerColor
global opponentPlayerColor
if depth==0 or not isAnyMovePossible(board, currentPlayerColor) or not isAnyMovePossible(board,opponentPlayerColor):
if countPieces(board,currentPlayerColor)>7:
'''
initial stage and opening moves trying to focus on center and attack
'''
return (0.75 * evaluationMovingToCenter(board))+ (0.20 * evaluationAttackFunction(board))+(0.5*evaluationColorVsOpposite(board))
elif countPieces(board,currentPlayerColor)>=6 :
'''
middle stage have to be defensive And would also need to be attacktive
'''
return (0.75 * evaluationMovingToDefense(board))+ (0.15 * evaluationAttackFunction(board))+(0.5*evaluationColorVsOpposite(board)+(0.5*evaluationMakingItKing(board)))
elif countPieces(board,currentPlayerColor)>4 :
'''
middle stage have to be get in center amd attack
'''
return (0.20 * evaluationMovingToCenter(board))+ (0.70 * evaluationAttackFunction(board))+(0.5*evaluationColorVsOpposite(board)+(0.5*evaluationMakingItKing(board))+evaluationCanBeAttacked(board))
else:
'''
when 4 or less than four pieces are remaining
'''
return (0.50 * evaluationAttackFunction(board))+(0.30*evaluationColorVsOpposite(board)+(0.20*evaluationMovingToDefense(board))+evaluationCanBeAttacked(board))
if maximizingPlayer:
v = -sys.maxint - 1
moves = getAllPossibleMoves(board, opponentPlayerColor)
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)
#print "inside max",color
#color=getOpponentColor(color)
v = max(v,minimax(newBoard, depth-1, alpha, beta, False))
alpha = max(alpha,v)
if beta <= alpha:
return alpha
return v
else:
v = sys.maxint
moves = getAllPossibleMoves(board, currentPlayerColor)
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)
#print "inside min",color
#color=getOpponentColor(color)
v = min(v,minimax(newBoard, depth-1, alpha, beta, True))
beta = min(beta,v)
if beta <= alpha:
return beta
return v
def evaluation(board, color):
global myColor
color = myColor
global moveCount
opponentColor = gamePlay.getOpponentColor(color)
value = 0
global fourth_layer
# Loop through all board positions
for piece in range(1, 33):
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
# Eval on opponent pieces <= 7 or for first 10 moves(Be in attacking mode)
if board[x][y].upper() == color.upper() and (countPieces(board, opponentColor) <= 7 or moveCount <= 10):
value = value + 1
continue
elif board[x][y].upper() == opponentColor.upper() and (countPieces(board, opponentColor) <= 7 or moveCount <= 10):
value = value - 1
continue
# Eval on pieces in fourth layer(pieces will tend to move towards 4th outer most layer ie defensive positions)
if board[x][y].upper() == color.upper() and piece in fourth_layer:
value = value + 4
continue
elif board[x][y].upper() == opponentColor.upper() and piece in fourth_layer:
value = value - 4
continue
# Eval on pieces in third layer (pieces will tend to move towards 3rd layer on board)
if board[x][y].upper() == color.upper() and piece in third_layer:
value = value + 3
continue
elif board[x][y].upper() == opponentColor.upper() and piece in third_layer:
value = value - 3
continue
# Eval on pieces in second layer (pieces will tend to move towards 2nd layer on board
if board[x][y].upper() == color.upper() and piece in second_layer:
value = value + 2
continue
elif board[x][y].upper() == opponentColor.upper() and piece in second_layer:
value = value - 2
continue
# Eval on pieces in first layer (pieces will tend to move to center two pieces)
if board[x][y].upper() == color.upper() and piece in first_layer:
value = value + 1
continue
elif board[x][y].upper() == opponentColor.upper() and piece in first_layer:
value = value - 1
continue
return value
def iterativeDeepeningAlphaBetaPruning(board, time, maxRemainingMoves):
# Set depth limit depending the available time
global myColor
global opponentColor
# Don't call mini-max, return the best move at the game start according to the player's color.
if maxRemainingMoves == 150:
if myColor == 'r':
return [11, 15]
else:
return [22, 18]
moves = getAllPossibleMoves(board, myColor)
#return the only move, if any
if len(moves) == 1:
return moves[0]
depth = 4
myPieces = gamePlay.countPieces(board, myColor)
opponentPieces = gamePlay.countPieces(board, opponentColor)
# piece ratio for deciding the depth
pieceRatio = myPieces/opponentPieces
if pieceRatio < 1:
depth = 6
if time < 30 and pieceRatio < 1: depth = 3
elif time < 20 and pieceRatio > 1: depth = 2
elif time < 10: depth = 1
bestMove = None
best = -sys.maxint-1
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)
#Calling mini-max with alpha-beta pruning
moveVal = alphaBetaPruning(newBoard, depth,time)
if best == None or moveVal > best:
bestMove = move
best = moveVal
return bestMove
def minimax(board, depth, alpha, beta, maximizingPlayer):
global currentPlayerColor
global opponentPlayerColor
if depth == 0 or not isAnyMovePossible(
board, currentPlayerColor) or not isAnyMovePossible(
board, opponentPlayerColor):
if countPieces(board, currentPlayerColor) > 7:
'''
initial stage and opening moves trying to focus on center and attack
'''
return (0.75 * evaluationMovingToCenter(board)) + (
0.20 * evaluationAttackFunction(board)) + (
0.5 * evaluationColorVsOpposite(board))
elif countPieces(board, currentPlayerColor) >= 6:
'''
middle stage have to be defensive And would also need to be attacktive
'''
return (0.75 * evaluationMovingToDefense(board)) + (
0.15 * evaluationAttackFunction(board)) + (
0.5 * evaluationColorVsOpposite(board) +
(0.5 * evaluationMakingItKing(board)))
elif countPieces(board, currentPlayerColor) > 4:
'''
middle stage have to be get in center amd attack
'''
return (0.20 * evaluationMovingToCenter(board)) + (
0.70 * evaluationAttackFunction(board)) + (
0.5 * evaluationColorVsOpposite(board) +
(0.5 * evaluationMakingItKing(board)) +
evaluationCanBeAttacked(board))
else:
'''
when 4 or less than four pieces are remaining
'''
return (0.50 * evaluationAttackFunction(board)) + (
0.30 * evaluationColorVsOpposite(board) +
(0.20 * evaluationMovingToDefense(board)) +
evaluationCanBeAttacked(board))
if maximizingPlayer:
v = -sys.maxint - 1
moves = getAllPossibleMoves(board, opponentPlayerColor)
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard, move)
#print "inside max",color
#color=getOpponentColor(color)
v = max(v, minimax(newBoard, depth - 1, alpha, beta, False))
alpha = max(alpha, v)
if beta <= alpha:
return alpha
return v
else:
v = sys.maxint
moves = getAllPossibleMoves(board, currentPlayerColor)
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard, move)
#print "inside min",color
#color=getOpponentColor(color)
v = min(v, minimax(newBoard, depth - 1, alpha, beta, True))
beta = min(beta, v)
if beta <= alpha:
return beta
return v
