def minimax(board, color, time, alpha, beta, depth, maximizingPlayer,
movesRemaining):
moves = getAllPossibleMoves(board, color)
if depth == 0 or not moves:
return evaluation(board, color), []
if maximizingPlayer == True:
best = float("-inf")
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard, move)
alpha, x = minimax(newBoard, gamePlay.getOpponentColor(color),
time, best, beta, depth - 1, False,
movesRemaining)
if best == float("-inf") or alpha > best:
bestMove = move
best = alpha
if best >= beta:
break
return best, bestMove
else:
best = float("inf")
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard, move)
beta, x = minimax(newBoard, gamePlay.getOpponentColor(color), time,
alpha, best, depth - 1, True, movesRemaining)
if best == float("inf") or beta < best:
bestMove = move
best = beta
if alpha >= best:
break
return best, bestMove
def minimax(board, color, time, alpha, beta, depth, maximizingPlayer, movesRemaining):
moves = getAllPossibleMoves(board, color)
if depth == 0 or not moves:
return evaluation(board, color), []
if maximizingPlayer == True:
best = float("-inf")
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)
alpha, x = minimax(newBoard, gamePlay.getOpponentColor(color), time, best, beta, depth-1, False, movesRemaining)
if best == float("-inf") or alpha > best:
bestMove = move
best = alpha
if best >= beta:
break
return best, bestMove
else:
best = float("inf")
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)
beta, x = minimax(newBoard, gamePlay.getOpponentColor(color), time, alpha, best, depth-1, True, movesRemaining)
if best == float("inf") or beta < best:
bestMove = move
best = beta
if alpha >= best:
break
return best, bestMove
def safePawns(board, color):
opponentColor = gamePlay.getOpponentColor(color)
#get count of pawns on four edges for each player
safePawnsOfPlayer = board[0].count(color) + board[7].count(color) + getColumn(board, 0).count(color) + getColumn(board, 7).count(color)
safePawnsOfOpponent = board[0].count(opponentColor) + board[7].count(opponentColor) + getColumn(board, 0).count(opponentColor) + getColumn(board, 7).count(opponentColor)
scoreMap['safePawns'] = safePawnsOfPlayer-safePawnsOfOpponent
def safeKings(board, color):
opponentColor = gamePlay.getOpponentColor(color)
#get count of kings on four edges for each player
safeKingsOfPlayer = board[0].count(color.upper()) + board[7].count(color.upper()) + getColumn(board, 0).count(color.upper()) + getColumn(board, 7).count(color.upper())
safeKingsOfOpponent = board[0].count(opponentColor.upper()) + board[7].count(opponentColor.upper()) + getColumn(board, 0).count(opponentColor.upper()) + getColumn(board, 7).count(opponentColor.upper())
scoreMap['safeKings'] = safeKingsOfPlayer - safeKingsOfOpponent
def activeDefenseForKings(board, color):
opponentColor = gamePlay.getOpponentColor(color)
val1 = 0
val2 = 0
forwardRight = (1, -1)
forwardLeft = (1, 1)
backwardRight = (-1, -1)
backwardLeft = (-1, 1)
postNeighbors = list()
postNeighbors = [backwardRight, backwardLeft]
for i in range(1,33):
xy = gamePlay.serialToGrid(i)
x = xy[0]
y = xy[1]
if board[x][y] == color:
#for post neighbors of kings, see if there are same colored pawns to rate defensiveness
for item in postNeighbors:
coord = tuple(sum(x) for x in zip(xy, item))
prev_xy = gridToSerial(coord[0], coord[1])
if prev_xy in range (1, 33):
new_coord = gamePlay.serialToGrid(prev_xy)
newx = new_coord[0]
newy = new_coord[1]
if board[newx][newy] == color:
val1 += 1
elif board[x][y] == opponentColor.upper():
val2 += 1
return val1*val2
def evaluation7(board, color):
opponentColor = gamePlay.getOpponentColor(color)
list1 = [6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27]
value = 0
# Loop through all board positions
for piece in list1:
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
if board[x][y].upper() == color.upper():
if board[x-1][y-1].upper() == color.upper():
value = value + 1
elif board[x-1][y+1].upper() == color.upper():
value = value + 1
elif board[x+1][y-1].upper() == color.upper():
value = value + 1
elif board[x+1][y+1].upper() == color.upper():
value = value + 1
elif board[x][y].upper() == opponentColor.upper():
if board[x-1][y-1].upper() == opponentColor.upper():
value = value - 1
elif board[x-1][y+1].upper() == opponentColor.upper():
value = value - 1
elif board[x+1][y-1].upper() == opponentColor.upper():
value = value - 1
elif board[x+1][y+1].upper() == opponentColor.upper():
value = value - 1
# Let us give weightage 10
#return value * 10
return value
def evaluation5(board, color):
opponentColor = gamePlay.getOpponentColor(color)
# 0th element is for top row from top; 7th element is the bottom row in the board
list = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24], [25, 26, 27, 28], [29, 30, 31, 32]]
value = 0
# Loop through all board positions
for i in range(0, 8):
for j in range(0, 4):
piece = list[i][j]
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
if board[x][y].upper() == color.upper():
if color.upper() == 'R':
# When R pieces reach bottom base line get score 7
value = value + i
elif color.upper() == 'W':
# When W pieces reach top base line get score 7
value = value + (7-i)
elif board[x][y].upper() == opponentColor.upper():
if opponentColor.upper() == 'R':
# When opponent's R pieces reach bottom base line get score -7
value = value - i
elif opponentColor.upper() == 'W':
# When opponent's W pieces reach top base line get score -7
value = value - (7-i)
# Lets give weightage 2
#return value * 2
return value
def offense(board, color):
opposideEnd = None;
opponentColor = gamePlay.getOpponentColor(color)
distanceMeasure = 0.0
count1 = 0
count2 = 0
if color == 'r':
for i in range(1, 33):
xy = gamePlay.serialToGrid(i)
x = xy[0]
y = xy[1]
if board[x][y] == color:
count1 += x
elif board[x][y] == opponentColor:
count2 += (7-x)
elif color == 'w':
for i in range(1,33):
xy = gamePlay.serialToGrid(i)
x = xy[0]
y = xy[1]
if board[x][y] == color:
count1 += (7-x)
elif board[x][y] == opponentColor:
count2 += x
scoreMap['offense'] = count1 - count2
def nextMove(board, color, time, movesRemaining):
global myColor
global opponentColor
myColor = color
opponentColor = gamePlay.getOpponentColor(color)
#Trying to find the move where I have best score
return iterativeDeepeningAlphaBetaPruning(board, time, movesRemaining)
def isFirstMove(board, color):
opponentColor = gamePlay.getOpponentColor(color)
value1 = 0
value2 = 0
# Loop through the middle two rows
for piece in range(13, 21):
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
if board[x][y].upper() == color.upper():
value1 = value1 + 1
elif board[x][y].upper() == opponentColor.upper():
value1 = value1 + 1
# Loop through all the rows
for piece in range(1, 33):
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
if board[x][y].upper() == color.upper():
value2 = value2 + 1
elif board[x][y].upper() == opponentColor.upper():
value2 = value2 + 1
# If it is first move middle two rows have one or less piece
if value2 == 24 and value1 <= 1:
return True
else:
return False
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 evaluation3(board, color):
# Evaluation Function 3(matrix): Corners on all sides are given utmost importance
# and hence my pawns will always tend to go towards the corner so that the pawns cannot be captured.
evalMatrix = [
[ 0, 4, 0, 4, 0, 4, 0, 4],
[ 4, 0, 3, 0, 3, 0, 3, 0],
[ 0, 3, 0, 2, 0, 2, 0, 4],
[ 4, 0, 2, 0, 1, 0, 3, 0],
[ 0, 3, 0, 1, 0, 2, 0, 4],
[ 4, 0, 2, 0, 2, 0, 3, 0],
[ 0, 3, 0, 3, 0, 3, 0, 4],
[ 4, 0, 4, 0, 4, 0, 4, 0]
]
opponentColor = gamePlay.getOpponentColor(color)
value = 0
for piece in range(1, 33):
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
if board[x][y].upper() == color.upper():
value = value + evalMatrix[x][y]
elif board[x][y].upper() == opponentColor.upper():
value = value - evalMatrix[x][y]
return value
def alphabetaPruning(board,color,alpha,beta, depth, maxval,movesRemaining):
if depth == 0 or movesRemaining == 0: # Checking whether I am in the end node, then I will return my evaluation value
return evaluation(board, maxColor)
moves = getAllPossibleMoves(board,color) # Getting all values of the new board that I passed from nextMove
opponentColor = gamePlay.getOpponentColor(color) # Getting the opponent color
test = float("inf") # Assigning value for infinity
if maxval: #Checking whether maxval is Max
value = -test #Assigning the value of -infnity to value
for move in moves: # looping through all the moves
evalBoard = deepcopy(board) #Taking the deepcopy of board
gamePlay.doMove(evalBoard,move)
#value = evaluation(evalBoard, color)
#print value, "VALUE"
value = max(value,alphabetaPruning(evalBoard,opponentColor,alpha,beta, depth-1, False,movesRemaining)) # performing recursive call and assigning the maximum value to the "value"
alpha = max(alpha, value) # Assigning the max value to alpha
if beta <= alpha: #checking whether beta is lesser than alpha
break
return value # returning the value
else:
value = test # Assigning the positive max value to alpha
for move in moves: # iterating through all the values
evalBoard = deepcopy(board) #Making a deepcopy
#value = evaluation(evalBoard, color)
gamePlay.doMove(evalBoard,move)
value = min(value,alphabetaPruning(evalBoard,opponentColor,alpha,beta, depth-1, True, movesRemaining)) # Performing the recursive call and assigning the minimum value to "value"
#value = min(move, alphabetaPruning(move, depth-1, True))
beta = min(beta, value) # Assiging the minumum value to beta by comparing beta and value
if beta <= alpha: # checking whether beta is lesser than alpha
break
return value # returning the value
def evaluation(board, color, depth, time):
# This function adds the value obtained from each of the evaluation functions(which has their respective weights) and return a final value
# When depth is even, max value gets evaluated, hence we send our color to each of the evaluation function
# When depth is odd, min value gets evaluated, hence we send opponent's color to each of the evaluation function
# TIME UPDATE: when there is plenty of time, use all the evaluation functions but when time is less than 10 seconds, I am only making use of only 2 evaluation functions,
# namely, number of pawns and number of kings.
if time > 10:
if depth%2 == 0:
value = 0.35 * evaluation1(board,color) + 0.20 * evaluation2(board,color) + 0.15 * evaluation3(board,color) + 0.2 * evaluation4(board,color) + 0.1 * evaluation5(board, color)
else:
value = 0.35 * evaluation1(board,gamePlay.getOpponentColor(color)) + 0.20 * evaluation2(board,gamePlay.getOpponentColor(color)) + 0.15 * evaluation3(board,gamePlay.getOpponentColor(color)) + 0.2 * evaluation4(board,gamePlay.getOpponentColor(color)) + 0.1 * evaluation5(board, gamePlay.getOpponentColor(color))
else:
if depth%2 == 0:
value = 0.6 * evaluation1(board,color) + 0.4 * evaluation2(board, color)
else:
value = 0.6 * evaluation1(board,gamePlay.getOpponentColor(color)) + 0.4 * evaluation2(board, gamePlay.getOpponentColor(color))
return value
def evaluation(board):
global mycolor
color = mycolor
opponentColor = gamePlay.getOpponentColor(color)
pawn, king, corner, edge, front, defence, defended_pawns = 0, 0, 0, 0, 0, 0, 0
for piece in range(1, 33):
x, y = gamePlay.serialToGrid(piece)
coin = board[x][y]
#pawns and kings
if coin.upper() == color.upper():
pawn += 1
if coin == color.upper():
king += 1
else:
pawn -= 1
if coin == opponentColor.upper():
king -= 1
#corner - safe against attacks
if piece in (4,29):
if color == coin: corner += 1
else: corner -= 1
#edge - edges are safe, so coins must try to go there
if piece in (5,13,21,12,20,28):
if color == coin: edge += 1
else: edge -= 1
#front - if a pawn is <= 3 steps from becoming a king
if coin == 'r':
if piece > 20:
if color == 'r': front += 1
else: front -= 1
else:
if piece < 13:
if color == 'w': front += 1
else: front -= 1
#defended_pawns - if a pawn cannot be attacked
if coin == 'r' and x > 0 and y > 0 and y < 7 and board[x-1][y+1].upper() == coin.upper() and board[x-1][y-1].upper() == coin.upper():
if color == 'r': defended_pawns += 1
else: defended_pawns -= 1
elif x < 7 and y > 0 and y < 7 and board[x+1][y+1].upper() == coin.upper() and board[x+1][y-1].upper() == coin.upper():
if color == 'w': defended_pawns += 1
else: defended_pawns -= 1
#my_defence - the bottom row must not be moved unless opponent arrives, provides the best defence
for piece in range(1, 5):
if color == coin == 'r': defence += 1
for piece in range(29, 33):
if color == coin == 'w': defence += 1
# Order of importance(descending) is 1.last layer defence 2.number of kings,
#3.number of pawns, 4.defend most pawns, 5.corner, 6.edges, 7.attack(front)
return (3*pawn + 7*king + 0.34*corner + 0.33*edge + 0.2*front + 25*defence + 0.5*defended_pawns)
def nextMove(board, color, time, movesRemaining):
moves = getAllPossibleMoves(board, color)
if len(moves) == 1:
return moves[0]
opponentColor = gamePlay.getOpponentColor(color)
equalMoves = []
best = None
alpha = None
beta = float("inf")
# If the time remaining < 3 seconds, then just apply simpleGreedy and increase depth according to time
if time < 3:
depth = 1
elif time < 10:
depth = 2
elif time < 30:
depth = 4
else:
if movesRemaining > 40:
depth = 8
else:
depth = 6
for move in moves: # this is the max turn(1st level of minimax), so next should be min's turn
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)
#Beta is always inf here as there is no parent MIN node. So no need to check if we can prune or not.
moveVal = evaluation(newBoard, color, depth, 'min', opponentColor, alpha, beta)
if best == None or moveVal > best:
bestMove = move
best = moveVal
equalMoves = []
equalMoves.append(move)
elif moveVal == best:
equalMoves.append(move)
if best > alpha:
alpha = best
#So the equalMoves consists of all the moves that have ended up with same value after Minimax evaluation
if len(equalMoves) > 1:
#The below logic tries to see if there is any next move that will form a defensive structure from the
#equalMoves list and returns it.
for move in equalMoves:
l = len(move)
xy = gamePlay.serialToGrid(move[l-1])
x = xy[0]
y = xy[1]
if (x+1) <= 7:
if (y+1) <= 7 and board[x+1][y+1].lower() == color.lower():
return move
if (y-1) >= 0 and board[x+1][y-1].lower() == color.lower():
return move
if (x-1) >= 0:
if (y+1) <= 7 and board[x-1][y+1].lower() == color.lower():
return move
if (y-1) >= 0 and board[x-1][y-1].lower() == color.lower():
return move
return bestMove
def nextMove(board, color, time, movesRemaining):
global my_color, init_time, time_set, init_moves
my_color = color
print "My move turn\n"
moves = getAllPossibleMoves(board, color)
#Trying to find the move where I have best score
bestMove=None
if len(moves)==1: ## return the move when only a single move is present
bestMove = moves[0]
print "return the only move left\n"
else: ## more than one possible move is present.
best = None
depth =0
heuristic = curr_evaluation
alpha = -float('inf')
beta = float('inf')
if not time_set: ## recording the time given so as to split into intervals
time_set = True
init_time = time
init_moves=movesRemaining
##print init_time, "W##########"
if init_time*3/4 <=time and time<init_time: ##game is in the first quarter
if movesRemaining>146:
depth = random.randrange(2,4)
elif movesRemaining>138 and movesRemaining <=146:
depth =5
elif movesRemaining > init_moves*2/3 and movesRemaining<=138:
depth=6
else:
depth=5
elif init_time/2 <=time and time< init_time*3/4: ## game is in the second quarter
if movesRemaining>init_moves/3 and movesRemaining< 2*init_moves/3:
depth = random.randrange(3,5)
elif movesRemaining>0 and movesRemaining <=init_moves/3:
depth = random.randrange(4,6)
#heuristic =
elif time>0 and time<= init_time/4:
if movesRemaining>init_moves/3 and movesRemaining< 2*init_moves/3:
depth = random.randrange(4,7)
elif movesRemaining>0 and movesRemaining <=init_moves/3:
depth = random.randrange(6,8)
else:
depth=5
##heuristic =
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)
moveVal = miniMax(newBoard,depth,float('inf'), alpha, True, gamePlay.getOpponentColor(color), heuristic) ### we have already evaluated Max's childs here so, its Min's turn to make a move on each of these childs, so min turn is true.
if best == None or moveVal > best:
bestMove = move
best = moveVal
alpha = moveVal
return bestMove
def nextMove(board, col, time, movesRemaining):
#print "player color",color
global currentPlayerColor
currentPlayerColor=col
global opponentPlayerColor
opponentPlayerColor = getOpponentColor(col)
moves = getAllPossibleMoves(board, currentPlayerColor)
'''If there is only 1 move possible no need to evaluate just return that moves'''
if len(moves)==1:
return moves[0]
else:
best = None
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)
alpha = -sys.maxint - 1
beta = sys.maxint
'''
Time and moves remaining are few based on the simple greedy evaluation of the board
'''
if time < 5 or (movesRemaining)<4:
moveVal = evaluation1(newBoard)
else:
'''
#if moves remaing are very few we donot go too deep
'''
if (movesRemaining/2)<30:
moveVal = minimax(newBoard,3,alpha,beta,True)
'''inital moves'''
elif movesRemaining>140:
#start of the game heuristics handles the opening so dont need to go to deep
moveVal = minimax(newBoard,3,alpha,beta,True)
else:
'''last moves'''
if time < 5:
moveVal = minimax(newBoard,1,alpha,beta,True)
elif time<18:
moveVal = minimax(newBoard,3,alpha,beta,True)
elif time<23:
moveVal = minimax(newBoard,5,alpha,beta,True)
elif time<28:
moveVal = minimax(newBoard,7,alpha,beta,True)
'''middle moves'''
else:
moveVal = minimax(newBoard,5,alpha,beta,True)
#moveVal = minimax(newBoard,depth,alpha,beta,True) #we cal minimax to evaluate
if best == None or moveVal > best:
bestMove = move
best = moveVal
return bestMove
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 nextMove(board, col, time, movesRemaining):
global deep
global timeList
#Assigned global current and opponent color used to expand nodes and evaluate as per the player
global currentPlayerColor
currentPlayerColor=col
global opponentPlayerColor
opponentPlayerColor = getOpponentColor(col)
moves = getAllPossibleMoves(board, currentPlayerColor)#returns a list of possible moves[] for the current player
#Trying to find the move where my game has best score
if len(moves)==1:
return moves[0]
timeList.append(time)
elapsed=timeList[0]-time
originalTime = elapsed + time
print "timeList" , timeList
if(len(timeList)>=2):
print "secondLast", timeList[-2]
differenceTime = timeList[-2]-timeList[-1]
else:
differenceTime=0
print "last",timeList[-1]
best = None
#if time==0 or ((differenceTime*2) >= timeList[-1]) or countPieces(board, currentPlayerColor)<= differenceTime:
#or time differnce between last and second last
if time<=3 or movesRemaining>=time:
newBoard = deepcopy(board)
print "calling random move"
bestMove = randomMove(newBoard,currentPlayerColor)
else:
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)#get the possible states
depth=deep
alpha = -sys.maxint - 1
beta = sys.maxint
print "calling next move"
moveVal = minimax(newBoard,deep,alpha,beta,True)
if best == None or moveVal > best:
bestMove = move
best = moveVal
if deep<=7:
deep=deep+1
print "increased depth,new depth is ",deep
#timeList.append(time)
print"time list after eval",timeList
return bestMove
def getOpponentsLonerPieces(board, color):
listOfLoners = list()
opponentColor = gamePlay.getOpponentColor(color)
#find loners of opponent
for i in range(1,33):
xy = gamePlay.serialToGrid(i)
x = xy[0]
y = xy[1]
if board[x][y] == opponentColor.upper() or board[x][y] == opponentColor:
listOfLoners.append(i)
return listOfLoners
def nextMove(board, col, time, movesRemaining):
#print "player color",color
global currentPlayerColor
currentPlayerColor = col
global opponentPlayerColor
opponentPlayerColor = getOpponentColor(col)
moves = getAllPossibleMoves(board, currentPlayerColor)
'''If there is only 1 move possible no need to evaluate just return that moves'''
if len(moves) == 1:
return moves[0]
else:
best = None
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard, move)
alpha = -sys.maxint - 1
beta = sys.maxint
'''
Time and moves remaining are few based on the simple greedy evaluation of the board
'''
if time < 5 or (movesRemaining) < 4:
moveVal = evaluation1(newBoard)
else:
'''
#if moves remaing are very few we donot go too deep
'''
if (movesRemaining / 2) < 30:
moveVal = minimax(newBoard, 3, alpha, beta, True)
'''inital moves'''
elif movesRemaining > 140:
#start of the game heuristics handles the opening so dont need to go to deep
moveVal = minimax(newBoard, 3, alpha, beta, True)
else:
'''last moves'''
if time < 5:
moveVal = minimax(newBoard, 1, alpha, beta, True)
elif time < 18:
moveVal = minimax(newBoard, 3, alpha, beta, True)
elif time < 23:
moveVal = minimax(newBoard, 5, alpha, beta, True)
elif time < 28:
moveVal = minimax(newBoard, 7, alpha, beta, True)
'''middle moves'''
else:
moveVal = minimax(newBoard, 5, alpha, beta, True)
#moveVal = minimax(newBoard,depth,alpha,beta,True) #we cal minimax to evaluate
if best == None or moveVal > best:
bestMove = move
best = moveVal
return bestMove
def cornerSquares(board, color): drawPositions = [5, 1, 32, 28] value = 0 opponentColor = gamePlay.getOpponentColor(color) for item in drawPositions: xy = gamePlay.serialToGrid(item) x = xy[0] y = xy[1] if board[x][y] == color or board[x][y] == color.upper(): value += 1 elif board[x][y] == opponentColor or board[x][y] == opponentColor.upper(): value -= 1 scoreMap['cornerSquare'] = value
def defenders(board, color):
opponentColor = gamePlay.getOpponentColor(color)
defendersOfPlayer = 0
defendersOfOpponent = 0
#get count of pawns and kings on edge rows for each player
if color == 'w':
defendersOfPlayer = board[0].count(color) + board[0].count(color.upper()) + board[1].count(color) + board[1].count(color.upper())
defendersOfOpponent = board[6].count(color) + board[6].count(color.upper()) + board[7].count(color) + board[7].count(color.upper())
else:
defendersOfPlayer = board[6].count(color) + board[6].count(color.upper()) + board[7].count(color) + board[7].count(color.upper())
defendersOfOpponent = board[0].count(color) + board[0].count(color.upper()) + board[1].count(color) + board[1].count(color.upper())
scoreMap['defenders'] = 10*defendersOfPlayer-10*defendersOfOpponent
def attackers(board, color):
opponentColor = gamePlay.getOpponentColor(color)
attackersOfPlayer = 0
attackersOfOpponent = 0
#get count of pawns and kings on top most rows for each player
if color == 'w':
attackersOfPlayer = board[2].count(color) + board[2].count(color.upper()) + board[3].count(color) + board[3].count(color.upper()) + board[4].count(color) + board[4].count(color.upper())
attackersOfOpponent = board[5].count(color) + board[5].count(color.upper()) + board[4].count(color) + board[4].count(color.upper()) + board[3].count(color) + board[3].count(color.upper())
else:
attackersOfOpponent = board[2].count(color) + board[2].count(color.upper()) + board[3].count(color) + board[3].count(color.upper()) + board[4].count(color) + board[4].count(color.upper())
attackersOfPlayer = board[5].count(color) + board[5].count(color.upper()) + board[4].count(color) + board[4].count(color.upper()) + board[3].count(color) + board[3].count(color.upper())
scoreMap['attackers'] = attackersOfPlayer-attackersOfOpponent
def getValueBasedOnCoinDiff(board, x, y, color):
opponentColor = gamePlay.getOpponentColor(color)
# Count how many pieces I have more than the opponent
value = 0
if board[y][x] == color:
value = value + ALIVE_POINT
elif board[y][x] == opponentColor:
value = value - ALIVE_POINT
elif board[y][x] == color.upper():
value = value + ALIVE_POINT * 2
elif board[y][x] == opponentColor.upper():
value = value - ALIVE_POINT * 2
return value
def nextMove(board, color, time, movesRemaining):
global myColor
global opponentColor
myColor = color
opponentColor = gamePlay.getOpponentColor(color)
#result = minimax_simple(board, color, 7, True)
result = minimax_pruning(board, 5, -1000000000000, 1000000000000, True)
#print "$"*40
#print result[0]
#print time, "\t", movesRemaining
return result[0]
def safeKings(board, color):
opponentColor = gamePlay.getOpponentColor(color)
corner = [5, 1, 32, 28, 29, 4]
value = 0
for king in corner:
xy = gamePlay.serialToGrid(king)
x = xy[0]
y = xy[1]
if board[x][y] == color.upper():
value += 1
return value
def evaluation2(board,color):
#Evaluation Function 2: Counting the number of kings of each player in the current state
opponentColor = gamePlay.getOpponentColor(color)
value = 0
for piece in range(1, 33):
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
if board[x][y] == "R" or board[x][y] == "W":
if board[x][y] == color:
value = value + 1.5
elif board[x][y] == opponentColor:
value = value - 1.5
return value
def evaluation1(board, color):
# Evaluation Function 1: Counting the number of pawns each player has in the current state
opponentColor = gamePlay.getOpponentColor(color)
value = 0
for piece in range(1, 33):
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
if board[x][y] == color:
value = value + 1
elif board[x][y] == opponentColor:
value = value - 1
return value
def unoccupiedFieldsOnPromotion(board, color):
opponentColor = gamePlay.getOpponentColor(color)
value = 0
if color == 'r':
oppRow = [29,30,31,32]
else:
oppRow = [1,2,3,4]
for item in oppRow:
xy = gamePlay.serialToGrid(item)
x = xy[0]
y = xy[1]
if board[x][y] == opponentColor or board[x][y] == opponentColor.upper():
value -= 1
scoreMap['unoccpromo'] = value
def pawnsOnDiagonal(board, color):
diagonal = [9,14,18,23]
opponentColor = gamePlay.getOpponentColor(color)
value = 0
#loop through pieces on diagonals and find out pawns which are at the risk of being captured
for item in diagonal:
xy = gamePlay.serialToGrid(item)
x = xy[0]
y = xy[1]
if board[x][y] == color or board[x][y] == color.upper():
value += 1
elif board[x][y] == opponentColor or board[x][y] == opponentColor.upper():
value -= 1
scoreMap['pawnsOnDiagonal'] = value
def miniMax(board, depth, beta, alpha, minPlayerTurn, color, heuristic): ## returns best value for max or min
moves = getAllPossibleMoves(board, color)
if depth==0 or len(moves)==0: ## base condition return if depth is reached or if there are no moves present
return heuristic(board)
if minPlayerTurn==False: # Max player
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)
alpha= max(alpha, miniMax(newBoard, depth-1, beta, alpha, True, gamePlay.getOpponentColor(color), heuristic)) ###recursive call to Min player
if alpha >=beta: ## prune
break
return alpha
elif minPlayerTurn==True:
## Min Player's turn
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard, move)
beta= min(beta, miniMax(newBoard, depth-1, beta, alpha, False, gamePlay.getOpponentColor(color), heuristic)) ###recursive call to Max player
if alpha >=beta: # prune
break
return beta
def isMyCapturePossibleFromPosition(board, x, y):
# Returns whether (x,y) piece can make a capture at this time
opponent = getOpponentColor(board[x][y])
# Check whether a jump possible to all four directions
if canMoveToMyPosition(board, x, y, x-2, y-2) == True:#top left diagonal leaving 1
return True
if canMoveToMyPosition(board, x, y, x-2, y+2) == True:#bottom left
return True
if canMoveToMyPosition(board, x, y, x+2, y-2) == True:#right top
return True
if canMoveToMyPosition(board, x, y, x+2, y+2) == True:#right bottom
return True
return False
def scoreKings(board, color):
opponentColor = gamePlay.getOpponentColor(color)
value = 0
# Loop through all board positions
for piece in range(1, 33):
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
if board[x][y] == color.upper():
value = value + 2
elif board[x][y] == opponentColor.upper():
value = value - 2
scoreMap['kings'] = value
def isMyCapturePossibleFromPosition(board, x, y):
# Returns whether (x,y) piece can make a capture at this time
opponent = getOpponentColor(board[x][y])
# Check whether a jump possible to all four directions
if canMoveToMyPosition(board, x, y, x - 2,
y - 2) == True: #top left diagonal leaving 1
return True
if canMoveToMyPosition(board, x, y, x - 2, y + 2) == True: #bottom left
return True
if canMoveToMyPosition(board, x, y, x + 2, y - 2) == True: #right top
return True
if canMoveToMyPosition(board, x, y, x + 2, y + 2) == True: #right bottom
return True
return False
def alphaBetaPruning(node, depth, time):
moves = getAllPossibleMoves(node[0], node[2])
bestMove = moves[0]
#to set the lower bound at max node
bestScore = -float("inf")
for move in moves:
newBoard = deepcopy(node[0])
gamePlay.doMove(newBoard,move)
#creating a new node from the initial board generation
newNode = returnList(newBoard, node[1]+1, gamePlay.getOpponentColor(node[2]))
#calls the min function next
score = minimum(newNode, depth, -float("inf"), float("inf"), time)
if score > bestScore:
bestMove = move
bestScore = score
return bestMove
def nextMove(board, color, time, movesRemaining):
moves = getAllPossibleMoves(board, color)
opponentColor = gamePlay.getOpponentColor(color)
depth = 5
best = None
alpha = None
beta = float("inf")
for move in moves: # this is the max turn(1st level of minimax), so next should be min's turn
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)
#Beta is always inf here as there is no parent MIN node. So no need to check if we can prune or not.
moveVal = evaluation(newBoard, color, depth, 'min', opponentColor, alpha, beta)
if best == None or moveVal > best:
bestMove = move
best = moveVal
if best > alpha:
alpha = best
return bestMove
def evaluation2(board, color):
opponentColor = gamePlay.getOpponentColor(color)
value = 0
# Loop through all board positions
for piece in range(1, 33):
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
if board[x][y] == color.upper():
value = value + 1
elif board[x][y] == opponentColor.upper():
value = value - 1
# Let us give extra weightage 10
#return value * 10
return value
def evaluation(board, color):
# Evaluation function 1
# Count how many more pieces I have than the opponent
opponentColor = gamePlay.getOpponentColor(color)
value = 0
# Loop through all board positions
for piece in range(1, 33):
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
if board[x][y].upper() == color.upper():
value = value + 1
elif board[x][y].upper() == opponentColor.upper():
value = value - 1
return value
def evaluation3(board, color):
opColor = gamePlay.getOpponentColor(color)
value = 0
# Loop through all board positions
for piece in range(1, 33):
# Check if I can capture two
# Check whether a jump possible to all four directions
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
if gamePlay.canMoveToPosition(board, x, y, x-2, y-2) == True:
if gamePlay.isCapturePossibleFromPosition(board, x-2, y-2):
if board[x][y].upper() == color.upper():
value = value + 1
elif board[x][y].upper() == opColor.upper():
value = value - 1
if gamePlay.canMoveToPosition(board, x, y, x-2, y+2) == True:
if gamePlay.isCapturePossibleFromPosition(board, x-2, y+2):
if board[x][y].upper() == color.upper():
value = value + 1
elif board[x][y].upper() == opColor.upper():
value = value - 1
if gamePlay.canMoveToPosition(board, x, y, x+2, y-2) == True:
if gamePlay.isCapturePossibleFromPosition(board, x+2, y-2):
if board[x][y].upper() == color.upper():
value = value + 1
elif board[x][y].upper() == opColor.upper():
value = value - 1
if gamePlay.canMoveToPosition(board, x, y, x+2, y+2) == True:
if gamePlay.isCapturePossibleFromPosition(board, x+2, y+2):
if board[x][y].upper() == color.upper():
value = value + 1
elif board[x][y].upper() == opColor.upper():
value = value - 1
# Lets give weightage 10
# Let us give extra weightage 10
return value
def nextMove(board, col, time, movesRemaining):
#print "player color",color
global currentPlayerColor
currentPlayerColor=col
global opponentPlayerColor
opponentPlayerColor = getOpponentColor(col)
moves = getAllPossibleMoves(board, currentPlayerColor)#returns a list of possible moves[]
#Trying to find the move where I have best score
best = None
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)#get the possible states
depth=5
alpha = -sys.maxint - 1
beta = sys.maxint
moveVal = minimax(newBoard,depth,alpha,beta,True) #we cal minimax to evaluate
if best == None or moveVal > best:
bestMove = move
best = moveVal
return bestMove
def gamePhase(board, color):
opponentColor = gamePlay.getOpponentColor(color)
value2 = 0
# Loop through all the rows
for piece in range(1, 33):
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
if board[x][y].upper() == color.upper():
value2 = value2 + 1
elif board[x][y].upper() == opponentColor.upper():
value2 = value2 + 1
if value2 > 18 and value2 <= 24:
return 1
elif value2 > 10 and value2 <= 18:
return 2
else:
return 3
def minimax(board, maxP, color, alpha, beta, depth):
#calculates maximum when maxP is True and minimum otherwise
infty = float('inf')
moves = getAllPossibleMoves(board, color)
opponentColor = gamePlay.getOpponentColor(color)
if maxP:
if moves == [] or depth == 0:
return [], evaluation(board)
best_score = -infty
best_move = moves[0]
for move in moves:
boardTemp = deepcopy(board)
gamePlay.doMove(boardTemp,move)
m,score = minimax(boardTemp,False,opponentColor,alpha,beta,depth-1)
if score > best_score:
best_move = move
best_score = score
alpha = max(alpha,score)
#Beta cut-off
if alpha >= beta:
break
else:
if moves == [] or depth == 0:
return [], evaluation(board)
best_score = infty
best_move = moves[0]
for move in moves:
boardTemp = deepcopy(board)
gamePlay.doMove(boardTemp,move)
m,score = minimax(boardTemp,True,opponentColor,alpha,beta,depth-1)
if score < best_score:
best_move = move
best_score = score
beta = min(beta,score)
#Alpha cut-off
if alpha >= beta:
break
return best_move, best_score
def evaluation4(board, color):
opponentColor = gamePlay.getOpponentColor(color)
value = 0
# Loop through all board positions in the list with weightage 4, 3, 2, 1
# The follwoing positions are at edge across the square board
#list1 = [1, 2, 3, 4, 5, 12, 13, 20, 21, 28, 29, 30, 31, 32]
# This is 2 level deep from the edge across the square board
#list2 = [6, 7, 8, 9, 16, 17, 24, 25, 26, 27]
# This is 3 level deep from the edge across the square board
list3 = [10, 11, 14, 19, 22, 23]
# This is 4 level deep from the edge across the square board
list4 = [15, 18]
for piece in list3:
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
if board[x][y].upper() == color.upper():
value = value + 1
elif board[x][y].upper() == opponentColor.upper():
value = value - 1
for piece in list4:
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
if board[x][y].upper() == color.upper():
value = value + 2
elif board[x][y].upper() == opponentColor.upper():
value = value - 2
# Lets give weightage 3
#return value * 3
return value
def nextMove(board, color, time, movesRemaining):
possibleMoves = getAllPossibleMoves(board, color)
opponentColor = gamePlay.getOpponentColor(color)
#Trying to find the move where I have best score
bestValue = None
bestmove = possibleMoves[0]
depth = 5
alpha = float("-inf")
beta = float("inf")
for move in possibleMoves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move) #
#retrieves the value from minChance.
#Here, alpha is the lower bound of the actual MiniMax value and beta is the upper bound of the actual MiniMax value.
score = minChance(newBoard, depth-1,color, opponentColor, alpha, beta) # This is the max turn(1st level of minimax), so next should be min's turn
if bestValue == None or score > bestValue:
#If the better score is found than the current best value then that move is considered as the best move. Hence, it is taken.
bestMove = move
bestValue = score
if bestValue > alpha:
alpha = bestValue
return bestMove
def nextMove(board, color, time, movesRemaining):
# I will have different depth level as per time remaining
if time > 120:
depth = 8
elif time > 60:
depth = 6
elif time > 5:
depth = 4
else:
depth = 2
#print "DBG", phase, depth, time
#t1 = datetime.datetime.now()
moves = getAllPossibleMoves(board, color)
best = None
opColor = gamePlay.getOpponentColor(color)
# If only one move available no need for any computations
if len(moves) == 1:
bestMove = moves[0]
return bestMove
# Strategy1: If it is first move hardcoding the best move
if isFirstMove(board, color):
if color.upper() == 'R':
bestMove = [11, 15]
else:
bestMove = [22, 18]
return bestMove
# Strategy2: Remove moves that lead to opponent captures my piece
for move in moves:
myBoard = deepcopy(board)
gamePlay.doMove(myBoard,move)
# Check if opponents capturing position gone
if gamePlay.isCapturePossible(myBoard, opColor) == True:
moves.remove(move)
# Strategy3: Opponent captures one, but I capture double
# Check if newBoard is giving Opponent a capture
opMoves = getAllPossibleMoves(board, opColor)
# At most of the capture, opMoves will have one element
if gamePlay.isCapturePossible(board, opColor) == True:
opMove = opMoves[0]
opBoard = deepcopy(board)
gamePlay.doMove(opBoard, opMove)
myMoves = getAllPossibleMoves(opBoard, color)
# Check if I can capture two pieces
if len(myMoves) > 0:
myMove = myMoves[0]
# At double-capture, lenghth of the move will be >2
if len(myMove) > 2:
# Do nothing, let opponent capture
pass
else:
# Try to block the capture
for move in moves:
myBoard = deepcopy(board)
gamePlay.doMove(myBoard,move)
opMoves = getAllPossibleMoves(myBoard, opColor)
#Check if opponents capturing position gone
if not gamePlay.isCapturePossible(board, opColor):
bestMove = move
return bestMove
# Now start the main MiniMax and alpha-beta here
for move in moves:
# Strategy4: If I can double-capture, thats best move, kind of greedy
if len(move) > 2:
bestMove = move
return bestMove
newBoard = deepcopy(board)
alpha = -float('inf')
beta = float('inf')
# Calling with different depth depending on time remaining in the game
alphaVal = alphaBeta(newBoard, move, depth, alpha, beta, True, color, opColor)
if best == None or alphaVal > best:
bestMove = move
best = alphaVal
#t2 = datetime.datetime.now()
return bestMove