def minimax_simple(node, depth, maximizingPlayer):
global myColor
global opponentColor
color = opponentColor
if maximizingPlayer:
color = myColor
if depth == 0 or isTerminal(node, color):
return (None, heuristic(node))
bestMove = None
moves = getAllPossibleMoves(node, color)
if maximizingPlayer:
bestValue = -1000000000000
for move in moves:
newBoard = deepcopy(node)
gamePlay.doMove(newBoard,move)
moveVal = minimax_simple( newBoard, depth-1, False)
if bestValue < moveVal[1]:
bestValue = moveVal[1]
bestMove = move
else:
bestValue = 1000000000000
for move in moves:
newBoard = deepcopy(node)
gamePlay.doMove(newBoard,move)
moveVal = minimax_simple( newBoard, depth-1, True)
if bestValue > moveVal[1]:
bestValue = moveVal[1]
bestMove = move
return (bestMove, bestValue)
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 traverseTree(parnt, board, color, depth):
#print "color",color
global alpha
global beta
global localalpha
global flag
moves = getMoves(board, color)
if depth == globalDepth or len(moves) == 0:
value = valueBoard(board)
parnt[1] = value
return
#print "moves",moves
#if len(moves) == 0:
# return
#iCounter=0
for move in moves:
#print "iCounter :",iCounter
flag = True
node = makeNode(parnt, None, move[0], move[1])
#print "a"
newBoard = deepcopy(board)
#print "b"
gamePlay.doMove(newBoard, color, move)
#print "c"
#gamePlay.printBoard(newBoard)
#print newBoard
oppColor = gamePlay.opponent(color)
traverseTree(node, newBoard, oppColor, depth + 1)
#if traverseTreeReturn=="depth" or traverseTreeReturn=="pass":
#gamePlay.printBoard(newBoard)
#value=valueBoard(newBoard)
#print "value",node[0][1]
if node[0][1] == None:
node[0][1] = node[1]
if depth % 2 == 0:
localalpha = node[1]
if depth % 2 == 1:
beta = node[1]
elif node[0][1] <= node[1] and depth % 2 == 0:
node[0][1] = node[1]
if localalpha != None and localalpha < node[1]:
localalpha = node[1]
elif node[0][1] >= node[1] and depth % 2 == 1:
node[0][1] = node[1]
if beta != None and beta > node[1]:
beta = node[1]
if alpha != None and beta != None:
if alpha >= beta and flag == True:
flag = False
break
if localalpha != None and beta != None:
if localalpha >= beta and flag == True:
flag = False
break
return
def nextMove(board, color, time, movesRemaining):
depth = 8
global myColor
myColor = color
global moveCount
moveCount += 1
# if time left <=60 reduce depth to 5
if time <= 60:
depth = 5
# during initial moves keep depth to 4
if moveCount <= 4:
depth = 4
moves = getAllPossibleMoves(board, color)
# if possible moves is just one, dont call minmax.
if len(moves) == 1:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,moves[0])
return moves[0]
val, bestMove = minimax(board, color, time, float("-inf"), float("inf"), depth, True, movesRemaining)
return bestMove
def max_stage(board, depth_limit, cur_depth, player):
#Base case of recursive call
if cur_depth == depth_limit:
return eval_func(board, player)
#print "Inside Max Stage: Player ", player
# Find all possible moves
moves = generate_possible_moves(board, player)
#No possible moves. Game over. Return to find the winner
if len(moves) == 0:
return "pass"
else:
highest_minimax_value = float('-inf')
best_move = moves[0]
for move in moves:
#print "Move Max: ", move, cur_depth
cur_board = deepcopy(board)
gamePlay.doMove(cur_board, player, move)
#Recursive call to minimax_strategy to go to deeper node
cur_minimax_value = min_stage(cur_board, depth_limit,
(cur_depth + 1), flip_player(player))
#print "Current Max: ",cur_minimax_value
if cur_minimax_value > highest_minimax_value:
#print "Selected Max: ", cur_minimax_value
highest_minimax_value = cur_minimax_value
best_move = move
#print "Final Choice Max:" , highest_minimax_value, cur_depth
return highest_minimax_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):
return evaluation(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 expandHelper(board,color,x,y,lst):
if(myValidMove(board,color,(x,y))):
#print x,y,color
newboard=np.array(board);
#make move:
doMove(newboard,color,(x,y));
lst.append(((x,y),newboard));
def nextMove(board, color, time, movesRemaining):
depth = 8
global myColor
myColor = color
global moveCount
moveCount += 1
# if time left <=60 reduce depth to 5
if time <= 60:
depth = 5
# during initial moves keep depth to 4
if moveCount <= 4:
depth = 4
moves = getAllPossibleMoves(board, color)
# if possible moves is just one, dont call minmax.
if len(moves) == 1:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard, moves[0])
return moves[0]
val, bestMove = minimax(board, color, time, float("-inf"), float("inf"),
depth, True, movesRemaining)
return bestMove
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 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 alphabeta(board, alpha, beta, depth, color, player):
can_moves = []
for i in range(8):
for j in range(8):
if gamePlay.valid(board, player, (i,j)):
can_moves.append((i,j))
if depth == 0 or len(can_moves) == 0:
return calculate_heuristic(board, color)
if(player == color):#Maximizing
v = -999999
for can_move in can_moves:
temp_board = copy.deepcopy(board)
gamePlay.doMove(temp_board, player, can_move)
v = max(v, alphabeta(temp_board, alpha, beta, depth-1, color, gamePlay.opponent(player)))
alpha = max(alpha, v)
if(alpha >= beta):
break
return v
else:#Minimizing
v = 999999
for can_move in can_moves:
temp_board = copy.deepcopy(board)
gamePlay.doMove(temp_board, player, can_move)
v = min(v, alphabeta(temp_board, alpha, beta, depth-1, color, gamePlay.opponent(player)))
beta = min(beta, v)
if (alpha >= beta):
break
return v
def nextMove(board, color, time): best_val = None best_move = None moves = [] for row in range(8): for col in range(8): if gamePlay.valid(board, color, (row, col)): moves.append((row, col)) #shuffle the moves in case it places the same position in every game #shuffle(moves) if len(moves) == 0: return "pass" if moves == "pass": return "pass" opp = gamePlay.opponent(color) #evaluate max's position and choose the best value if color == "B": for move in moves: newBoard = board[:] gamePlay.doMove(newBoard, color, move) #alpha = - INFINITY beta = INFINITY #we want to choose the max one if best_val = max(best_val, alpha_beta(newBoard, opp, 3, -INFINITY, INFINITY)): #update best move best_move = move
def minimax_strategy(board, depth_limit, cur_depth, player):
#print "Inside minimax_strategy"
# Find all possible moves
moves = generate_possible_moves(board, player)
#No possible moves. Game over. Return to find the winner
if len(moves) == 0:
return "pass"
else:
best_minimax_value = float('-inf')
best_move = moves[0]
for move in moves:
cur_board = deepcopy(board)
gamePlay.doMove(cur_board, player, move)
#print "Move:" , move
#Recursive call to minimax_strategy to go to deeper node
cur_minimax_value = min_stage(cur_board, depth_limit,
(cur_depth + 1), flip_player(player))
#cur_minimax_value = minimax_strategy(cur_board, depth_limit, (cur_depth+1), best_minimax_value, flip_player(player))
if cur_minimax_value > best_minimax_value:
best_minimax_value = cur_minimax_value
best_move = move
return best_move
def nextMove(board, color, time): root = Node() # 루트노드 생성 for i in range(8): for j in range(8): if valid(board, color, (i, j)): # 놓을 수 있는 곳이면 자식으로 추가 temp = Node() temp.data = (i,j) temp.board = deepcopy(board) doMove(temp.board, color, temp.data) root.children.append(temp) if len(root.children) == 0: return "pass" makeTree(root, color, 1) # 트리 생성 alphabeta(root, limitDepth, -INF, INF, MAX, color) # assign cost maxx = -INF bestmove = 0 n = len(root.children) for i in range(n): # 최대 이익을 선택 if maxx < root.children[i].cost: maxx = root.children[i].cost bestmove = root.children[i].data return bestmove
def resultOfAction(kBoard, kColor, kNextMove):
"""
return board after applying the action specified by move
"""
changedBoard = deepcopy(kBoard)
doMove(changedBoard, kColor, kNextMove)
return changedBoard
def expandHelper(board,color,x,y,lst):
global visitedStates;
if(myValidMove(board,color,(x,y))):
#print x,y,color
newboard=np.array(board);
#make move:
doMove(newboard,color,(x,y));
lst.append(newboard);
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 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 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 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 nextMove(board, color, time, movesRemaining):
moves = getAllPossibleMoves(board, color)
# Trying to find the move where I have best score
best = bestMove = None
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)
moveVal = getMoveVal(newBoard, getOpponentColor(color), time, movesRemaining, 4, False, 0, sys.maxint)
if best == None or moveVal > best:
bestMove = move
best = moveVal
return bestMove
def nextMove(board, color, time, movesRemaining):
moves = getAllPossibleMoves(board, color)
#Trying to find the move where I have best score
best = None
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard, move)
moveVal = evaluation(newBoard, color)
if best == None or moveVal > best:
bestMove = move
best = moveVal
return bestMove
def nextMove(board, color, time, movesRemaining):
moves = getAllPossibleMoves(board, color)
#Trying to find the move where I have best score
best = None
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)
moveVal = evaluation(newBoard, color)
if best == None or moveVal > best:
bestMove = move
best = 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 nextMove(board, color, timeDuration, reversed = False):
'''Function called to play the next move from gamePlay.py'''
global initialTimeStamp # Changing the scope to Global for the variable
global currentTimeStamp # Changing the scope to Global for the variable
global timeBuffer # Changing the scope to Global for the variable
if timeDuration > 1: # Resetting the Time Buffer based on the Time duration passed from Game Play
timeBuffer = 1
initialTimeStamp = time.time() # Capturing the time stamp at the moment nextMove is called
childMoves = possibleChildMoves(board, color) # Get the possible valid moves for the board
if len(childMoves) == 0: # If there are no more valid moves, return Pass
return "pass"
if len(childMoves) == 1 : # If there is only One valid move, return it
return childMoves[0]
totalPlayedTiles = totalPlayed(board) # Find the total number of pieces on the board.
# Based on the total number pieces on the board, determine the depth limit for the minimax
if totalPlayedTiles < 20: # If the total number of turns is less than 20, set the depth limit to 2
depthLimit = 2
#If the total turns is in the range of 20 and 50, set the limit to 4 or 2 based on time
elif totalPlayedTiles >= 20 and totalPlayedTiles < 50:
if timeDuration < 10: # If the remaining time is less than 10 seconds, set the depth limit to 2. Else, 4
depthLimit = 2
else:
depthLimit = 4
#If the total turns is greater than 50, set the limit to 6 or 4 based on time
else :
if timeDuration < 15: # If the remaining time is less than 15 seconds, set the depth limit to 4. Else, 6
depthLimit = 4
else:
depthLimit = 6
bestScore = 0 # Initialize the best score to 0
bestMove = childMoves[0] # Initialize the best move to be the first move of the list
for move in childMoves: # For every valid move, perform the following
currentTimeStamp = time.time() # Capture the current time stamp
timeElapsed = currentTimeStamp - initialTimeStamp # Find the time elapsed since the call from Game Play
if timeElapsed > (timeDuration - timeBuffer): # Check if the time elapsed is more than the allowed duration minus buffer
return bestMove
newBoard = deepcopy(board) # Make a copy of the current board
gamePlay.doMove(newBoard,color,move) # Play the move to check if its best using the upcoming operations
# Call the Minimax - Alpha Beta Pruning algorithm with Depth Limit, Alpha, Beta, Opponent Player and Maximizing Flag as False
score = miniMaxAlphaBeta(newBoard, depthLimit-1,-10000,10000, opponent(color) , False, timeDuration)
if score > bestScore: # If the returned score is better than the best Score, update the Best Score and Move
bestMove = move
bestScore = score
return bestMove # Return the best move
def evaluation(board, color, depth, turn, opponentColor, alpha, beta):
if depth > 1: #Comes here depth-1 times and goes to else for leaf nodes.
depth -= 1
opti = None
if turn == 'max':
moves = getAllPossibleMoves(board, color) #Gets all possible moves for player
for move in moves:
nextBoard = deepcopy(board)
gamePlay.doMove(nextBoard,move)
if beta > opti:
value = evaluation(nextBoard, color, depth, 'min', opponentColor, alpha, beta)
if value > opti: #None is less than everything and anything so we don't need opti == None check
opti = value
if opti > alpha:
alpha = opti
elif turn == 'min':
moves = getAllPossibleMoves(board, opponentColor) #Gets all possible moves for the opponent
for move in moves:
nextBoard = deepcopy(board)
gamePlay.doMove(nextBoard,move)
if alpha == None or opti == None or alpha < opti: #None conditions are to check for the first times
value = evaluation(nextBoard, color, depth, 'max', opponentColor, alpha, beta)
if opti == None or value < opti: #opti = None for the first time
opti = value
if opti < beta:
beta = opti
return opti # opti will contain the best value for player in MAX turn and worst value for player in MIN turn
else: #Comes here for the last level i.e leaf nodes
value = 0
for piece in range(1, 33):
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
#Below, we count the number of kings and men for each color.
#A player king is 1.5 times more valuable than a player man.
#An opponent king is 1.5 times worse for the player than an opponent man.
#By assigning more weight on kings, the AI will prefer killing opponent kings to killing opponent men.
#It will also prefer saving player kings to saving player men when the situation demands.
#If a player king is double the value of a man, then AI may choose to sacrifice a man to make a king.
#To avoid this, a factor of 1.5 has been chosen.
if board[x][y] == color.lower():
value += 2
elif board[x][y] == opponentColor.lower():
value -= 2
elif board[x][y] == color.upper():
value += 3
elif board[x][y] == opponentColor.upper():
value -= 3
return value
def evaluation(board, color, depth, turn, opponentColor, alpha, beta):
if depth > 1: #Comes here depth-1 times and goes to else for leaf nodes.
depth -= 1
opti = None
if turn == 'max':
moves = getAllPossibleMoves(board, color) #Gets all possible moves for player
for move in moves:
nextBoard = deepcopy(board)
gamePlay.doMove(nextBoard,move)
if beta > opti:
value = evaluation(nextBoard, color, depth, 'min', opponentColor, alpha, beta)
if value > opti: #None is less than everything and anything so we don't need opti == None check
opti = value
if opti > alpha:
alpha = opti
elif turn == 'min':
moves = getAllPossibleMoves(board, opponentColor) #Gets all possible moves for the opponent
for move in moves:
nextBoard = deepcopy(board)
gamePlay.doMove(nextBoard,move)
if alpha == None or opti == None or alpha < opti: #None conditions are to check for the first times
value = evaluation(nextBoard, color, depth, 'max', opponentColor, alpha, beta)
if opti == None or value < opti: #opti = None for the first time
opti = value
if opti < beta:
beta = opti
return opti # opti will contain the best value for player in MAX turn and worst value for player in MIN turn
else: #Comes here for the last level i.e leaf nodes
value = 0
for piece in range(1, 33):
xy = gamePlay.serialToGrid(piece)
x = xy[0]
y = xy[1]
#Below, we count the number of kings and men for each color.
#A player king is 1.5 times more valuable than a player man.
#An opponent king is 1.5 times worse for the player than an opponent man.
#By assigning more weight on kings, the AI will prefer killing opponent kings to killing opponent men.
#It will also prefer saving player kings to saving player men when the situation demands.
#If a player king is double the value of a man, then AI may choose to sacrifice a man to make a king.
#To avoid this, a factor of 1.5 has been chosen.
if board[x][y] == color.lower():
value += 2
elif board[x][y] == opponentColor.lower():
value -= 2
elif board[x][y] == color.upper():
value += 3
elif board[x][y] == opponentColor.upper():
value -= 3
return value
def handleError(board, color, time, movesRemaining):
#fall back to simple greedy, when there is an error in the code.. :)
moves = getAllPossibleMoves(board, color)
#Trying to find the move where I have best score
best = None
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)
moveVal = evaluation(newBoard, color)
if best == None or moveVal > best:
bestMove = move
best = moveVal
return bestMove
def nextMove(board, color, time, reversed=False):
global myColor
global opponentColor
global alpha
global beta
global localalpha
opponentColor = gamePlay.opponent(color)
myColor = color
moves = getMoves(board, color)
#print "moves :",moves
if len(moves) == 0:
return "pass"
childNodes = []
changeTime(time)
changeWeight(board)
bestMove = None
bestValue = None
#print "moves :",len(moves)
for move in moves:
parnt = makeNode(None, None, move[0], move[1])
childNodes.append(parnt)
newBoard = deepcopy(board)
gamePlay.doMove(newBoard, color, move)
#gamePlay.printBoard(newBoard)
#gamePlay.printBoard(newBoard)
oppColor = gamePlay.opponent(color)
traverseTree(parnt, newBoard, oppColor, 1)
#print "aaaaaaa ",aaaa
if bestMove == None:
bestMove = move
bestValue = parnt[1]
elif bestValue < parnt[1]:
bestMove = move
bestValue = parnt[1]
if alpha == None:
alpha = beta
if alpha < beta:
alpha = beta
beta = None
localalpha = None
#print parnt[1]
#for n in childNodes:
# print "final value :",n[1]
#print "bestValue ",bestValue
#print "bestMove ",bestMove
#iop=raw_input()
return bestMove
def nextMove(board, color, time, reversed = False): global myColor global opponentColor global alpha global beta global localalpha opponentColor=gamePlay.opponent(color) myColor=color moves=getMoves(board,color) #print "moves :",moves if len(moves) == 0: return "pass" childNodes=[] changeTime(time) changeWeight(board) bestMove=None bestValue=None #print "moves :",len(moves) for move in moves: parnt = makeNode(None,None,move[0],move[1]) childNodes.append(parnt) newBoard = deepcopy(board) gamePlay.doMove(newBoard,color,move) #gamePlay.printBoard(newBoard) #gamePlay.printBoard(newBoard) oppColor=gamePlay.opponent(color) traverseTree(parnt,newBoard,oppColor,1) #print "aaaaaaa ",aaaa if bestMove==None: bestMove=move bestValue=parnt[1] elif bestValue<parnt[1]: bestMove=move bestValue=parnt[1] if alpha == None: alpha=beta if alpha<beta: alpha=beta beta=None localalpha=None #print parnt[1] #for n in childNodes: # print "final value :",n[1] #print "bestValue ",bestValue #print "bestMove ",bestMove #iop=raw_input() return bestMove
def simple_greedy(board, color, reversed = False): '''Simple greedy strategy''' moves = get_successors(board, color) if len(moves) == 0: return "pass" best = None for move in moves: new_board = deepcopy(board) gamePlay.doMove(new_board, color, move) move_value = value(new_board, color) if best == None or better_than(move_value, best, color, reversed): best_move = move best = move_value return best_move
def nextMove(board, color, reversed = False):
moves = []
for i in range(8):
for j in range(8):
if gamePlay.valid(board, color, (i,j)):
moves.append((i,j))
if len(moves) == 0:
return "pass"
best = None
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,color,move)
moveVal = value(newBoard)
if best == None or betterThan(moveVal, best, color, reversed):
bestMove = move
def nextMove(board,color,time): decrease=0 #decrease the depth of alpha beta prunning. if time>=63.98:#at first, whichever piece we take, it is not a big deal.so we should save time at beginning. decrease=2 elif time<17 and time>=10: decrease=1 elif time<10: #when time is less than 10s decrease=2 elif time<=0: return "pass" moves=[] bonus=[] for i in range(8): for j in range(8): if gamePlay.valid(board,color,(i,j)): moves.append((i,j)) if len(moves)==0: return "pass" best=-100 alpha=-100 beta=100 if (0,0) in moves: bonus.append((0,0)) if(0,7) in moves: bonus.append((0,7)) if (7,0) in moves: bonus.append((7,0)) if (7,7) in moves: bonus.append((7,7)) if len(bonus)>0: for move in bonus: newBoard=deepcopy(board) gamePlay.doMove(newBoard,color,move) tmp=alphabeta(2-decrease,newBoard,alpha,beta,"MIN",color) if best<tmp: best=tmp bestMove=move else: for move in moves: newBoard=deepcopy(board) gamePlay.doMove(newBoard,color,move) tmp=alphabeta(5-decrease,newBoard,alpha,beta,"MIN",color) if best<tmp: best=tmp bestMove=move return bestMove
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):
validMoves = validMove(board, color)
if len(validMoves) == 0:
return "pass"
best = None
alpha = -1000
beta = 1000
for move in validMoves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard, color, move)
moveVal = alphabeta(newBoard, 3, alpha, beta, False, color,
reverseColor(color))
if best == None or best <= moveVal:
bestMove = move
best = moveVal
return bestMove
def maxChance(newBoard,depth,color,opponentColor,alpha,beta):
if depth == 0: # If the depth reaches the value 0, it means we have reached the leave node and now we need to estimate the value at that node using evaluation functions.
return evaluation(newBoard,color,opponentColor)
else:
maxScore = None
moves = getAllPossibleMoves(newBoard,color)
for move in moves:
nextBoard = deepcopy(newBoard)
gamePlay.doMove(nextBoard,move)
if beta > maxScore:
score = minChance(nextBoard,depth-1,color, opponentColor, alpha, beta) # This is the min turn, so next should be max's turn. This process continues recursively.
if score > maxScore:
maxScore = score
if maxScore > alpha:
alpha = maxScore
return maxScore
def alpha_beta(board, color): '''Alpha-beta pruning''' next_positions = get_successors(board, color) if len(next_positions) == 0: return "pass" next_color = gamePlay.opponent(color) best_value = -100000 best_move = (-1, -1) '''This for loop works as max_value(), so here starts the recursion with min_value()''' for pos in next_positions: tempboard = deepcopy(board) gamePlay.doMove(tempboard, color, pos) val = min_value(tempboard, next_color, 6) # Search depth is 6. If it is deeper, we need to wait too long for result if val >= best_value: best_value = val best_move = pos return best_move
def nextMove(board, color, time, reversed = False):
moves = []
for i in range(8):
for j in range(8):
if gamePlay.valid(board, color, (i,j)):
moves.append((i,j))
if len(moves) == 0:
return "pass"
best = None
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,color,move)
moveVal = value(newBoard)
if best == None or betterThan(moveVal, best, color, reversed):
bestMove = move
best = moveVal
return bestMove
def minimax(board, color, depth, alpha, beta, maximizingPlayer):
#By default if we do not get any move, we use "pass"
bestMove = "pass"
#Successor function called on current board
moves = successor(board, color)
if depth == 0 or moves== "pass":
return value(board, color, maximizingPlayer),"pass"
#Recursing over self and opponent
if maximizingPlayer:
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,color, move)
child = newBoard
x = minimax(child, gamePlay.opponent(color), depth - 1, alpha, beta, False)[0]
#alpha will select the maximum value
if alpha < x:
alpha = x
bestMove = move
#pruning condition
if beta <= alpha:
break
#maxplayer will always return the best move
return alpha,bestMove
#opponents best play
else:
for move in moves:
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,color, move)
child = newBoard
y = minimax(child, gamePlay.opponent(color), depth - 1, alpha, beta, True)[0]
#beta will select the minimum value
if beta > y:
beta = y
#pruning condition
if beta <= alpha:
break
#Doesn't matter what move we pass, this will never be returned to nextmove
return beta, "pass"
def minimize(board, alpha, beta, depth):
global opponentColor
global myColor
#Return a heurisitc based score once the depth limit is reached
if depth <=0 or not gamePlay.isAnyMovePossible(board, opponentColor):
return evaluate(board, myColor)
score = sys.maxint
for move in getAllPossibleMoves(board, myColor):
newBoard = deepcopy(board)
gamePlay.doMove(newBoard, move)
score = min(score, maximize(board, alpha, beta, depth-1))
#alpha cut-off
if score <= alpha:
return score
beta = min(beta, score)
return score
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 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 minChance(newBoard,depth,color,opponentColor,alpha,beta):
if depth == 0: #If the depth reaches the value 0, it means we have reached the leave node and now we need to estimate the value at that node using evaluation functions.
return evaluation(newBoard,color,opponentColor)
else:
minimumScore = None
moves = getAllPossibleMoves(newBoard, opponentColor)
for move in moves:
nextBoard = deepcopy(newBoard)
gamePlay.doMove(nextBoard,move)
#If the alpha score is less than the minimum score then rest of the nodes can be pruned.
if alpha == None or minimumScore == None or alpha < minimumScore: #None is less than everything and anything
score = maxChance(nextBoard,depth-1, color, opponentColor, alpha, beta) # This is the min turn, so next should be max's turn. This process continues recursively.
if minimumScore == None or score < minimumScore:
minimumScore = score
if minimumScore < beta:
beta = minimumScore
return minimumScore
def alphaBeta(board, move, depth, alpha, beta, maximizingPlayer, color, opColor):
if depth == 0:
# Different evaluation values multiplied by weightage
moveVal = evaluation1(board, color) * 10
if gamePhase(board, color) == 3:
kingVal = evaluation2(board, color) * 5
else:
kingVal = 0
capVal = evaluation3(board, color) * 10
posVal = evaluation4(board, color) * 1
promoVal = evaluation5(board, color) * 1
if gamePhase(board, color) != 0:
baseVal = evaluation6(board, color) * 1
else:
baseVal = 0
flockVal = evaluation7(board, color) * 2
# print moveVal, kingVal, capVal, posVal, promoVal, baseVal, flockVal
bestVal = moveVal + kingVal + capVal + posVal + promoVal + baseVal + flockVal
return bestVal
if (maximizingPlayer):
bestVal = -float('inf')
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)
moves = getAllPossibleMoves(newBoard, color)
for move in moves:
bestVal = max(bestVal, alphaBeta(newBoard, move, depth-1, alpha, beta, False, color, opColor))
alpha = max(alpha, bestVal)
if beta <= alpha:
break
return bestVal
else:
bestVal = float('inf')
newBoard = deepcopy(board)
gamePlay.doMove(newBoard,move)
moves = getAllPossibleMoves(newBoard, opColor)
for move in moves:
bestVal = min(bestVal, alphaBeta(newBoard, move, depth-1, alpha, beta, True, color, opColor))
beta = min(beta, bestVal)
if beta <= alpha:
break
return bestVal
def minimax(board, color, depth):
#Find the best move in the game
#if depth = 0, we calculate the score
if depth == 0:
return eval_fn(board, color)
#if game is over, we calculate the score
if gamePlay.gameOver(board):
return gamePlay.score(board)
best_val = None
best_move = None
opp = gamePlay.opponent(color)
# valid moves
moves = []
for row in range(8):
for col in range(8):
if gamePlay.valid(board, color, (row,col)):
moves.append((row,col))
#shuffle the moves in case it places the same position in every game
#shuffle(moves)
if len(moves) == 0:
return "pass"
if move == "pass":
return eval_fn(board, color)
#try each move in valid moves
#evaluate max's position and choose the best value
if color == "B":
for move in moves:
newBoard = board[:]
gamePlay.doMove(newboard, color, move)
val = minimax(newBoard, opp, depth-1)
if best_val is None or val > (best_val, best_move)[0]:
(best_val, best_move) = (val, move)
#evaluate min's position and choose the best value
if color == "W":
for move in moves:
newBoard = board[:]
gamePlay.doMove(newboard, color, move)
val = minimax(newBoard, opp, depth-1)
if best_val is None or val < (best_val, best_move)[0]:
(best_val, best_move) = (val, move)
return (best_val, best_move)[0]
def alpha_beta(board, color, depth, alpha, beta): """Find the utility value of the game and the best_val move in the game.""" if depth == 0: return eval_fn(board, color) if gamePlay.gameOver(board): return gamePlay.score(board) moves = [] for row in range(8): for col in range(8): if gamePlay.valid(board, color, (row, col)): moves.append((row, col)) #shuffle the moves in case it places the same position in every game #shuffle(moves) if len(moves) == 0: return "pass" if moves == "pass": return eval_fn(board, color) opp = gamePlay.opponent(color) # try each move #evaluate max's position and choose the best value if color == "B": for move in moves: newBoard = board[:] gamePlay.doMove(newBoard, color, move) #cut off the branches alpha = max(alpha, alpha_beta(newBoard, opp, depth-1, alpha, beta)) if beta <= alpha: return return alpha #evaluate min's position and choose the best value if color == "W": for move in moves: newBoard = board[:] gamePlay.doMove(newBoard, color, move) #cut off the branches beta = min(beta, alpha_beta(newBoard, opp, depth-1, alpha, beta)) if beta <= alpha: return return beta
def max_stage_with_pruning(board, depth_limit, cur_depth, player, alpha, beta,
time_lmt, start_time):
cur_time = time.time()
#Base case of recursive call
if cur_depth == depth_limit or (cur_time - start_time) > (time_lmt - 1):
return eval_func(board, player)
#print "Inside Max Stage: Player ", player
# Find all possible moves
moves = generate_possible_moves(board, player)
#No possible moves. Game over. Return to find the winner
if len(moves) == 0:
return "pass"
else:
highest_minimax_value = float('-inf')
best_move = moves[0]
for move in moves:
#print "Move Max: ", move, cur_depth
cur_board = deepcopy(board)
gamePlay.doMove(cur_board, player, move)
#Recursive call to minimax_strategy to go to deeper node
cur_minimax_value = min_stage_with_pruning(cur_board, depth_limit,
(cur_depth + 1),
flip_player(player),
alpha, beta, time_lmt,
start_time)
#print "Current Max: ",cur_minimax_value
if cur_minimax_value > highest_minimax_value:
#print "Selected Max: ", cur_minimax_value
highest_minimax_value = cur_minimax_value
best_move = move
cur_time = time.time()
if highest_minimax_value >= beta or (cur_time -
start_time) > (time_lmt - 1):
return highest_minimax_value
alpha = max(highest_minimax_value, alpha)
#print "Final Choice Max:" , highest_minimax_value, cur_depth
return highest_minimax_value
def miniMaxAlphaBeta(board , depth ,alpha,beta, color, maximizingPlayer,timeDuration):
'''Minimax algorithm to find the best move'''
currentTimeStamp = time.time() # Capturing the current time stamp
timeElapsed = currentTimeStamp - initialTimeStamp # Calculating the time elapsed since the call from Game Play
if depth == 0 or timeElapsed > (timeDuration - timeBuffer): # Check if the Depth has reached the limit
currHeurValue = heurValue(board, color) # If yes, return the heuristic value of the current board
return currHeurValue
childMoves = possibleChildMoves(board, color) # Get the possible valid moves for the board
if len(childMoves)==0: # Check if there are no more valid moves
currHeurValue = heurValue(board, color) # If yes, return the heuristic value of the current board
return currHeurValue
if maximizingPlayer == True: # Run if its Maximizing Players turn
for move in childMoves: # For every valid move, do the following
newBoard = deepcopy(board) # Create a copy of the current board
gamePlay.doMove(newBoard,color,move) # Play the move to check if its best using the upcoming operations
# Call the Minimax - Alpha Beta Pruning algorithm with the Depth, Alpha, Beta, Inverted Color and Maximizing Flag as False
result = miniMaxAlphaBeta(newBoard, depth-1,alpha,beta, opponent(color), False,timeDuration)
alpha = max(alpha, result) # Get the maximum of the alpha and result
if alpha >= beta: # Check if Alpha >= Beta
break # Prune, if True
return alpha # Return Alpha
if maximizingPlayer == False: # Run if its Minimizing Players turn
for move in childMoves: # For every valid move, do the following
newBoard = deepcopy(board) # Create a copy of the current board
gamePlay.doMove(newBoard,color,move) # Play the move to check if its best using the upcoming operations
# Call the Minimax - Alpha Beta Pruning algorithm with the Depth, Alpha, Beta, Inverted Color and Maximizing Flag as True
result = miniMaxAlphaBeta(newBoard, depth-1,alpha,beta, opponent(color), True,timeDuration)
beta = min(beta,result) # Get the maximum of the alpha and result
if beta <= alpha: # Check if Alpha >= Beta
break # Prune, if True
return beta # Return Beta
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 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 possibleChildMoves(board,color):
'''Function to get the valid moves for a Board'''
moves = [] # List to maintain the possible valid moves
orderedMovesValues = [] # List to maintain the moves and their heuristic values
orderedMoves = [] # List to maintain the ordered moves
for i in range(8):
for j in range(8):
if gamePlay.valid(board, color, (i,j)): # Check if the generated move is valid
moves.append((i,j)) # Append the move to the list
for move in moves :
newBoard = deepcopy(board) # Create a copy of the current board
gamePlay.doMove(newBoard,color,move) # Play the move to check its heuristic value
currHeurValue = heurValue(newBoard,color) # Find the heuristic value of the move
orderedMovesValues.append([currHeurValue,move]) # Append the move along with its heuristic value to a new list
orderedMovesValues.sort(key=lambda x: x[0],reverse = True) # Sort the list in Descending order based on heuristic value
for i in range(0,len(orderedMovesValues)):
orderedMoves.append(orderedMovesValues[i][1]) # Extract the set of moves from the Ordered Heuristic List
return orderedMoves # Return the Ordered Moves
def alphabeta(board, depth, alpha, beta, maximizingPlayer, playerColor,
currentColor):
validMoves = validMove(board, currentColor)
if depth == 0 or len(validMoves) == 0:
return value(board, playerColor)
# 플레이어인 경우 최대 점수(알파) 선택
if maximizingPlayer:
for move in validMoves:
tempBoard = deepcopy(board)
# 현재 색의 플레이어가 움직이게 함
gamePlay.doMove(tempBoard, currentColor, move)
# 다음 알파베타 프루닝을 위해 색 바꾸기
currentColor = reverseColor(currentColor)
alpha = max([
alpha,
alphabeta(tempBoard, depth - 1, alpha, beta, False,
playerColor, currentColor)
])
if alpha >= beta:
break
return alpha
# 상대인 경우 최소 점수(베타) 선택
else:
for move in validMoves:
tempBoard = deepcopy(board)
# 현재 색의 플레이어가 움직이게 함
gamePlay.doMove(tempBoard, currentColor, move)
# 다음 알파베타 프루닝을 위해 색 바꾸기
currentColor = reverseColor(currentColor)
beta = min([
beta,
alphabeta(tempBoard, depth - 1, alpha, beta, True, playerColor,
currentColor)
])
if alpha >= beta:
break
return beta
def alpha_beta_pruning_strategy(board, depth_limit, cur_depth, player, itime):
time_lmt = itime
start_time = time.time()
alpha = float('-inf')
beta = float('inf')
#print "Inside minimax_strategy"
# Find all possible moves
moves = generate_possible_moves(board, player)
#No possible moves. Game over. Return to find the winner
if len(moves) == 0:
return "pass"
else:
best_minimax_value = float('-inf')
best_move = moves[0]
for move in moves:
cur_board = deepcopy(board)
gamePlay.doMove(cur_board, player, move)
#print "Move:" , move
#Recursive call to minimax_strategy to go to deeper node
cur_minimax_value = min_stage_with_pruning(cur_board, depth_limit,
(cur_depth + 1),
flip_player(player),
alpha, beta, time_lmt,
start_time)
#cur_minimax_value = minimax_strategy(cur_board, depth_limit, (cur_depth+1), best_minimax_value, flip_player(player))
if cur_minimax_value > best_minimax_value:
best_minimax_value = cur_minimax_value
best_move = move
cur_time = time.time()
if best_minimax_value >= beta or (cur_time -
start_time) > (time_lmt - 1):
return best_move
alpha = max(best_minimax_value, alpha)
return best_move
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 changeB(cboard, Color, NextMove): #그수를 뒀다면 뒤집어지는 모든 상황들을 반영시키기
changeB = deepcopy(cboard)
doMove(changeB, Color, NextMove)
return changeB
