class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
def get_move(self, move):
if len(move) != 0:
self.board.make_move(move, self.opponent[self.color])
else:
self.color = 1
moves = self.board.get_all_possible_moves(self.color)
best_move = moves[0][0]
#self.board.make_move(best_move, self.color)
#best_score = self.board_score( self.color )
#self.board.undo()
move = self.minMax(self.color, 3, -999999999, best_move, 999999999, best_move)[1]
self.board.make_move(move, self.color)
return move
def minMax(self, player, depth, best_score, best_move, opponent_score, opponent_move):
if depth == 0:
return self.board_score( player ), best_move
# get all the moves of the current player
moves = self.board.get_all_possible_moves(player)
# Itterate through each move
for i in moves:
for ii in i:
# change to new game state
self.board.make_move(ii, player)
if (player == self.color):
opponent_score = self.minMax(self.opponent[self.color], depth-1, best_score, best_move,opponent_score, opponent_move)[0]
if (best_score < opponent_score):
best_score = opponent_score
best_move = ii
# opponent's turn: find the best score based on player's move
elif (player == self.opponent[self.color]):
best_score = self.minMax(self.color, depth-1, best_score, best_move,opponent_score, opponent_move)[0]
if (opponent_score > best_score):
opponent_score = best_score
opponent_move = ii
self.board.undo()
return best_score, best_move, opponent_score, opponent_move
def board_score(self, color):
## @param color: color of player making the move
## Heuristics to Evaluate with
## Normal Piece : 1000 pts
## King Piece : 2000 pts
## Rows away from enemy end if Normal : (rows - curr_row / rows) * 1000
## Amount of Pieces : (Amount of pieces left) / (self.col * self.p / 2) * 100
## Randomization : randomInt (0-10)
player_points = 0
opponent_points = 0
for c in range(self.col):
for r in range(self.row):
current_piece = self.board.board[c][r]
if current_piece.get_color() == color:
if current_piece.is_king == True:
player_points += 2000
else:
player_points += 1000
if color == 1:
player_points += ((self.row - r) / self.row) * 1000
else:
player_points += (r / self.row) * 1000
elif current_piece.get_color() == self.opponent[color]:
if current_piece.is_king == True:
opponent_points += 2000
else:
opponent_points += 1000
if self.opponent[color] == 1:
opponent_points += ((self.row - r) / self.row) * 1000
else:
opponent_points += (r / self.row) * 1000
else:
pass
if color == 1:
player_points += ((self.board.white_count / (self.col * self.p / 2)) * 100)
opponent_points += ((self.board.black_count / (self.col * self.p / 2)) * 100)
else:
player_points += ((self.board.black_count / (self.col * self.p / 2)) * 100)
opponent_points += ((self.board.white_count / (self.col * self.p / 2)) * 100)
randomization = randint(0, 50)
return player_points - opponent_points + randomization
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
# Returns optimal value for current player
def get_move(self, move):
alpha = -1000
value = -1000
beta = 1000
bestMove = None
if len(move) != 0: # If the opponent started first
self.board.make_move(move, self.opponent[self.color])
else:
self.color = 1
# Make a list of all possible moves that our AI can make
our_moves = self.board.get_all_possible_moves(self.color)
# Iterate through list of all our moves
for x in range(len(our_moves)):
for y in range(len(our_moves[x])):
# Make a move on the copy/theoretical board
self.board.make_move(our_moves[x][y], self.color)
currentScore = self.alphaBetaMin(alpha, beta, 1)
self.board.undo()
if currentScore >= value:
value = currentScore
bestMove = our_moves[x][y]
#print("New bestMove", bestMove, "current best score:", currentScore)
alpha = currentScore
#print("Decision?", bestMove)
self.board.make_move(bestMove, self.color)
return bestMove
def alphaBetaMin(self, alpha, beta, depth):
'''
# Check if our AI is black and we won
#if self.color == self.board.is_win(self.color):
if self.color == self.board.is_win("B"):
return 1000
# Check if our AI (black) lost
#elif self.color == 1 and self.board.is_win(self.color) == 2:
elif self.color == 1 and self.board.is_win("B") == 2:
return -1000
# Check if our AI (white) lost
#elif self.color == 2 and self.board.is_win(self.color) == 1:
elif self.color == 2 and self.board.is_win("W") == 1:
return -1000
# Check if opponent will tie
#if self.board.is_win(self.color) == -1:
if self.board.is_win("B") == -1:
return 0
'''
if depth == 3:
return self.get_heuristic_score2()
else:
value = 1000
# Go through every possible move
opponent_moves = self.board.get_all_possible_moves(
self.opponent[self.color])
for x in opponent_moves:
for move in x:
# Make move for opponent
self.board.make_move(move, self.opponent[self.color])
value = min(value,
self.alphaBetaMax(alpha, beta, depth + 1))
self.board.undo()
beta = min(beta, value)
if alpha >= beta:
return value
return value
def alphaBetaMax(self, alpha, beta, depth):
'''
# Check if our AI is black and we won
#if self.color == self.board.is_win(self.opponent[self.color]):
if self.color == self.board.is_win("B"):
return 1000
# Check if our AI (black) lost
#elif self.color == 1 and self.board.is_win(self.opponent[self.color]) == 2:
elif self.color == 1 and self.board.is_win("B") == 2:
return -1000
# Check if our AI (white) lost
#elif self.color == 2 and self.board.is_win(self.opponent[self.color]) == 1:
elif self.color == 2 and self.board.is_win("W") == 1:
return -1000
# Check if opponent will tie
#if self.board.is_win(self.opponent[self.color]) == -1:
if self.board.is_win("B") == -1:
return 0
'''
if depth == 3:
return self.get_heuristic_score2()
else:
value = -1000
# Go through every possible move
our_moves = self.board.get_all_possible_moves(self.color)
for x in our_moves:
for move in x:
self.board.make_move(move, self.color)
value = max(value,
self.alphaBetaMin(alpha, beta, depth + 1))
self.board.undo()
alpha = max(alpha, value)
if alpha >= beta:
return value
return value
def closeToBecomingKing(self, color, row_position):
if self.color == 1: # Our color is black
return row_position
else: # our color is white
return (self.board.row - row_position - 1)
def get_heuristic_score2(self):
num_black_kings = 0
num_white_kings = 0
num_safe_piece_black = 0
num_safe_piece_white = 0
num_back_black = 0
num_back_white = 0
closer_black = 0
closer_white = 0
#score = 0
for x in range(len(self.board.board)):
for y in range(len(self.board.board[x])):
# Check if it's our checker piece
if (self.board.board[x][y].get_color() == 'B'):
# Check if it's a king
if (self.board.board[x][y].is_king == True):
num_black_kings += 1
else: # Check how close checker piece is to becoming King
closer_black += self.closeToBecomingKing(self.color, x)
cp = self.board.board[x][y].get_location()
# Check if black checker piece is in the back
if (cp[0] == 0):
num_back_black += 1
# Check if it's an edge piece row 0, row n, col 0, col n
if (cp[0] == 0 or cp[0] == self.board.row - 1):
num_safe_piece_black += 1
if (cp[1] == 0 or cp[1] == self.board.col - 1):
num_safe_piece_black += 1
if (cp[0] == 0 and cp[1] == 0):
num_safe_piece_black -= 1
if (cp[0] == 0 and cp[1] == self.board.col - 1):
num_safe_piece_black -= 1
if (cp[0] == self.board.row - 1 and cp[1] == 0):
num_safe_piece_black -= 1
if (cp[0] == self.board.row - 1
and cp[1] == self.board.col - 1):
num_safe_piece_black -= 1
# Check for safe pieces that are not part of the edge
if (cp[0] != 0 and cp[0] != self.board.row - 1):
if (cp[1] != 0 and cp[1] != self.board.col - 1):
is_safe = True
if (self.board.board[x + 1][y -
1].get_color() == 'W'):
if (self.board.board[x -
1][y +
1].get_color() == '.'):
is_safe = False
if (self.board.board[x + 1][y +
1].get_color() == 'W'):
if (self.board.board[x -
1][y -
1].get_color() == '.'):
is_safe = False
if (self.board.board[x - 1][y + 1].get_color()
== 'W' and
self.board.board[x - 1][y + 1].is_king):
if (self.board.board[x +
1][y -
1].get_color() == '.'):
is_safe = False
if (self.board.board[x - 1][y - 1].get_color()
== 'W' and
self.board.board[x - 1][y - 1].is_king):
if (self.board.board[x +
1][y +
1].get_color() == '.'):
is_safe = False
if (is_safe == True):
#print("safe piece counted")
num_safe_piece_black += 1
#else:
#print(x, y)
#print("safe piece not counted")
#score -= 2
'''
# Check for safe pieces that are part of the edges
is_safe = True
# Check for safe piece on edge (column - 1)
if (cp[1] == self.board.col - 1):
if(self.board.board[x + 1][y - 1].get_color() == 'W'):
is_safe = False
# Check for safe piece on edge (0)
if (cp[1] == 0):
if(self.board.board[x + 1][y + 1].get_color() == 'W'):
is_safe = False
# check for safe piece on edge (column - 1) when a King
if (cp[1] == self.board.col - 1 and ((cp[0] > 0) or (cp[0] < self.board.row - 1))):
if(self.board.board[x - 1][y - 1].get_color() == 'W'):
is_safe = False
if(self.board.board[x + 1][y - 1].get_color() == 'W'):
is_safe = False
# check for safe piece on edge (0) when a King
if (cp[1] == 0 and ((cp[0] > 0) or (cp[0] < self.board.row - 1))):
if(self.board.board[x - 1][y + 1].get_color() == 'W'):
is_safe = False
if(self.board.board[x + 1][y + 1].get_color() == 'W'):
is_safe = False
if (is_safe == True):
num_safe_piece_black += 1
'''
elif (self.board.board[x][y].get_color() == 'W'):
if (self.board.board[x][y].is_king == True):
num_white_kings += 1
else:
closer_white += self.closeToBecomingKing(2, x)
# Check if it's a corner piece either (0, 0), (0, n), (n, 0), or (n, n)
cp = self.board.board[x][y].get_location()
# Check if white checker piece is in the back
if (cp[0] == self.board.row - 1):
num_back_white += 1
# Check if it's an edge piece row 0, row n, col 0, col n
if (cp[0] == 0 or cp[0] == self.board.row - 1):
num_safe_piece_white += 1
if (cp[1] == 0 or cp[1] == self.board.col - 1):
num_safe_piece_white += 1
if (cp[0] == 0 and cp[1] == 0):
num_safe_piece_white -= 1
if (cp[0] == 0 and cp[1] == self.board.col - 1):
num_safe_piece_white -= 1
if (cp[0] == self.board.row - 1 and cp[1] == 0):
num_safe_piece_white -= 1
if (cp[0] == self.board.row - 1
and cp[1] == self.board.col - 1):
num_safe_piece_white -= 1
# Check for white safe pieces that are not part of the edge
if (cp[0] != 0 and cp[0] != self.board.row - 1):
if (cp[1] != 0 and cp[1] != self.board.col - 1):
is_safe = True
if (self.board.board[x - 1][y -
1].get_color() == 'B'):
if (self.board.board[x +
1][y +
1].get_color() == '.'):
is_safe = False
if (self.board.board[x - 1][y +
1].get_color() == 'B'):
if (self.board.board[x +
1][y -
1].get_color() == '.'):
is_safe = False
if (self.board.board[x + 1][y + 1].get_color()
== 'B' and
self.board.board[x + 1][y + 1].is_king):
if (self.board.board[x -
1][y -
1].get_color() == '.'):
is_safe = False
if (self.board.board[x + 1][y - 1].get_color()
== 'B' and
self.board.board[x + 1][y - 1].is_king):
if (self.board.board[x -
1][y +
1].get_color() == '.'):
is_safe = False
if (is_safe == True):
num_safe_piece_white += 1
if self.color == 1:
score = 10 * (self.board.black_count - self.board.white_count)
#print("Score after diff in counts:", score)
#print('safe black:', num_safe_piece_black, 'safe white:', num_safe_piece_white, 'safe score:', num_safe_piece_black - num_safe_piece_white)
score += 5 * (num_black_kings - num_white_kings)
#print("Score after diff in Ks:", score)
#score += 2*(closer_black - closer_white)
score += 2 * (num_safe_piece_black - num_safe_piece_white)
#print("Score after diff in safe pieces:", score)
score += 2 * (num_back_black - num_back_white)
#print("Score after back row pieces:", score)
elif self.color == 2:
score = 10 * (self.board.white_count - self.board.black_count)
#print("Score after diff in counts:", score)
#print('safe black:', num_safe_piece_black, 'safe white:', num_safe_piece_white, 'safe score:', num_safe_piece_black - num_safe_piece_white)
score += 5 * (num_white_kings - num_black_kings)
#print("Score after diff in Ks:", score)
#score += 2*(closer_black - closer_white)
score += 2 * (num_safe_piece_white - num_safe_piece_black)
#print("Score after diff in safe pieces:", score)
score += 2 * (num_back_white - num_back_black)
#print("Score after back row pieces:", score)
return score
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
self.search_lim = 5
self.current_node = TreeNode(None, self.color)
def get_move(self, move):
if len(move) != 0:
# print("|" + str(move) + "|")
self.board.make_move(move, self.opponent[self.color])
#print("Player", self.opponent[self.color], "make move", move)
if len(self.current_node.child_node) != 0:
for child in self.current_node.child_node:
if str(child.move) == str(move):
self.current_node = child
else:
self.color = 1
self.current_node.player = self.color
for i in range(NS):
self.mcts(self.current_node)
#self.board.show_board()
#print("mcts counter:", i)
move = self.current_node.child_node[0]
for child in self.current_node.child_node:
if move.uct() < child.uct():
move = child
self.board.make_move(move.move, self.color)
# print("Player", self.color, "make move", move.move, "with a winrate of", move.winrate(), "simulated", move.simulation)
self.current_node = move
return move.move
def mcts(self, node):
if node.simulation >= minVisit:
#print("depth:", depth)
node.simulation += 1
if not len(node.child_node):
moves = self.board.get_all_possible_moves(node.player)
for move in moves:
for eachmove in move:
node.child_node.append(
TreeNode(eachmove, self.opponent[node.player],
node))
# proceed
next = self.mcts_selection(node)
self.board.make_move(next.move, node.player)
result = self.board.is_win(node.player)
if result:
if result == self.opponent[node.player]: node.win += 1
elif result == node.player:
next.win += 1
next.simulation += 1
self.board.undo()
return result
#self.board.show_board()
result = self.mcts(next)
self.board.undo()
# propagate up
if result == self.opponent[node.player]:
node.win += 1
return result
else:
result = self.simulate(node.player)
node.simulation += 1
if result == self.opponent[node.player]:
node.win += 1
#print("simulating", result)
return result
def mcts_selection(self, node): # Select optimal UCB node
current = node.child_node[0]
for child in node.child_node:
#print(current.uct())
if current.uct() < child.uct():
current = child
#print("player", node.player, "pick", current.move)
return current
def simulate(self, player):
win = 0
counter = 0
fake_board = Board(self.col, self.row, self.p)
self.copy_board(fake_board)
# print("DIT ME DIEN")
# fake_board.show_board()
# totaltime = 0
while win == 0:
moves = fake_board.get_all_possible_moves(player)
if len(moves) == 1:
index = 0
elif len(moves) == 0:
win = self.opponent[player]
break
else:
index = randint(0, len(moves) - 1)
if len(moves[index]) == 1:
inner_index = 0
else:
inner_index = randint(0, len(moves[index]) - 1)
move = moves[index][inner_index]
fake_board.make_move(move, player)
counter += 1
# bt = time.time()
if fake_board.tie_counter >= fake_board.tie_max:
win = -1
# totaltime += time.time() - bt
# print("self.board.is_win():", time.time() - bt)
player = self.opponent[player]
# #print("total time is_win:", totaltime)
# #bt = time.time()
# for i in range(counter):
# self.board.undo()
# #rint("total time undo:", time.time() - bt)
# fake_board.show_board()
return win
def copy_board(self, board):
"""
EZ game
:return: ez board
"""
board.tie_counter = self.board.tie_counter
board.tie_max = self.board.tie_max
board.board = copy.deepcopy(self.board.board)
board.saved_move = copy.deepcopy(self.board.saved_move)
board.black_count = self.board.black_count
board.white_count = self.board.white_count
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
def evalFunction(self, newBoard, color):
blackScore = 0
whiteScore = 0
totalPieces = 0
for x in range(0, newBoard.row):
for y in range(0, newBoard.col):
checker = newBoard.board[x][y]
if checker.get_color() == 'W':
if checker.is_king:
whiteScore += 5 + newBoard.row + 2
totalPieces += 1
else:
whiteScore += 5 + checker.row
totalPieces += 1
if checker.row == 0 or checker.row == newBoard.row:
whiteScore += 5
elif checker.get_color() == 'B':
if checker.is_king:
blackScore += 5 + newBoard.row + 2
totalPieces += 1
else:
blackScore += 5 + (newBoard.row - checker.row)
totalPieces += 1
if checker.col == 0 or checker.col == newBoard.col:
blackScore += 5
if color == 1:
return (blackScore - whiteScore) / totalPieces
else:
return (whiteScore - blackScore) / totalPieces
def alphaBeta(self, newBoard, depth, alpha, beta, maxPlayer):
# myScore = -9999
# enemyScore = 9999
#
# if maxPlayer:
# prevTurn = self.opponent[self.color]
# else:
# prevTurn = self.color
# if(newBoard.is_win(prevTurn)) or (newBoard.is_win(prevTurn) == 0):
# print(newBoard.is_win(prevTurn))
if depth == 0:
# return the score
# if maxPlayer:
score = self.evalFunction(newBoard, self.color)
return score # return the heuristic value of the board.
else:
# if it is max player, then for every child, choose the one with the max score
if maxPlayer:
score = -9999
# newMoves gets all possible moves (child nodes)
newMoves = newBoard.get_all_possible_moves(self.color)
for elem in newMoves:
for move in elem:
# print("max making move: ", move)
newBoard.make_move(move, self.color)
score = max(
score,
self.alphaBeta(newBoard, depth - 1, alpha, beta,
False))
# if score >= myScore:
# myScore = score
# score = max(score, self.alphaBeta(newBoard, depth - 1, alpha, beta, False))
alpha = max(alpha, score)
# prunes
if alpha >= beta: # beta cut off
# print("undo: ", move)
newBoard.undo()
break
# print("undo: ", move)
newBoard.undo()
return score
else:
# if min player, choose the minimum score
score = 9999
newMoves = newBoard.get_all_possible_moves(
self.opponent[self.color])
for elem in newMoves:
for move in elem:
# print("min making move: ", move)
newBoard.make_move(move, self.opponent[self.color])
score = min(
score,
self.alphaBeta(newBoard, depth - 1, alpha, beta,
True))
# score = min(score, self.alphaBeta(newBoard, depth - 1, alpha, beta, True))
# if score <= enemyScore:
# enemyScore = score
beta = min(beta, score)
# prunes
if alpha >= beta: # alpha cut off
# print("undo: ", move)
newBoard.undo()
break
# print("undo: ", move)
newBoard.undo()
return score
def get_move(self, move):
if len(move) != 0:
self.board.make_move(move, self.opponent[self.color])
else:
self.color = 1
moves = self.board.get_all_possible_moves(self.color)
bestScore = -9999
bestMove = Move([])
for elem in moves:
for move in elem:
# for elem in move:
# make deep copy of board
# print("max making move: ", move)
self.board.make_move(move, self.color)
score = self.alphaBeta(self.board, 3, -9999, 9999, False)
# print("score: ", score)
if score >= bestScore:
bestScore = score
bestMove = move
# print("undo: ", move)
self.board.undo()
# print("best move:", bestMove)
print("best score: ", bestScore)
self.board.make_move(bestMove, self.color)
return bestMove
class StudentAI():
def __init__(self,col,row,p):
self.col = col
self.row = row
self.p = p
self.board = Board(col,row,p)
self.board.initialize_game()
self.color = ''
self.opponent = {1:2,2:1}
self.color = 2
def get_move(self,move):
if len(move) != 0:
self.board.make_move(move,self.opponent[self.color])
else:
self.color = 1
bestVal = -999
bestMove = None
# if there is only one move to make, just make the move without evaluating
possible_moves = self.board.get_all_possible_moves(self.color)
if len(possible_moves) == 1 and len(possible_moves[0]) == 1:
self.board.make_move(possible_moves[0][0], self.color)
return possible_moves[0][0]
for moves in possible_moves:
for move in moves:
self.board.make_move(move, self.color)
val = self.search(1, StudentAI.switchColors(self.color), MIN, MAX)
self.board.undo()
if val > bestVal:
bestVal = val
bestMove = move
self.board.make_move(bestMove, self.color)
return bestMove
def search(self, depth, currentColor, alpha, beta):
if depth == 4 or self.board.is_win('B') or self.board.is_win('W'):
return self.evaluate(currentColor)
best = MIN if currentColor == self.color else MAX
for moves in self.board.get_all_possible_moves(currentColor):
for move in moves:
self.board.make_move(move, currentColor)
val = self.search(depth+1, StudentAI.switchColors(currentColor), alpha, beta)
self.board.undo()
if currentColor == self.color:
best = max(best, val)
alpha = max(alpha, best)
elif currentColor != self.color:
best = min(best, val)
beta = min(beta, best)
if beta <= alpha:
return best
return best
def piece_differential(self, currentColor):
if currentColor == 'B':
return self.board.black_count - self.board.white_count
return self.board.white_count - self.board.black_count
def evaluate(self, currentColor):
currentColor = 'B' if currentColor == 1 else 'W'
oppColor = 'W' if currentColor == 'B' else 'B'
# if we win in this game state, prefer to choose this path
# if the opponent wins in this game state, stay away from this path
if self.board.is_win(currentColor):
return 500
elif self.board.is_win(oppColor):
return -500
piece_location, kings = 0, 0
for i in range(self.board.row):
for j in range(self.board.col):
if (self.board.board[i][j].color == currentColor):
if self.board.board[i][j].is_king:
kings += 1
# we prefer the king to be in the middle of the board
if i <= self.row / 2:
piece_location += 7 + i
else:
piece_location += 7 + (self.board.row - i - 1)
else:
# we prefer the pawns to go to the opponent's side of the board
if self.board.board[i][j].color == 'B':
piece_location += 5 + i
else:
piece_location += 5 + (self.board.row - i - 1)
elif (self.board.board[i][j].color == oppColor):
if self.board.board[i][j].is_king:
kings -= 1
# we prefer the opponent's king to not be in the middle of the board
if i <= self.row / 2:
piece_location -= 7 + i
else:
piece_location -= 7 + (self.board.row - i - 1)
else:
# we prefer the opponent's pawns to not be on our side of the board
if self.board.board[i][j].color == 'B':
piece_location -= 5 + i
else:
piece_location -= 5 + (self.board.row - i - 1)
# if we have more kings, we prefer to play more aggressive
if kings > 0:
return piece_location + self.board.row * self.piece_differential(currentColor)
else:
return piece_location + self.piece_differential(currentColor)
@staticmethod
def switchColors(color):
if color == 1:
return 2
return 1
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
self.movecount = 0
self.simulate_times = 100
# self.file = f"{self.col}-{self.row}-{self.color}-data.txt"
# self.file = open(f"{self.col}-{self.row}-data.txt", "a")
def get_move(self, move):
self.movecount += 1
if len(move) != 0:
self.board.make_move(move, self.opponent[self.color])
else:
self.color = 1
self.file = f"{self.col}-{self.row}-{self.color}-data.txt"
moves = self.get_moves(self.board, self.color)
move = self.monte_carlo_tree(moves, self.simulate_times)
self.board.make_move(move, self.color)
return move
def monte_carlo_tree(self, moves: [], simulate_times: int):
s_parent = simulate_times * len(moves)
best_uct = -math.inf
best_move = 0
for move in moves:
wins = self.simulate(move, simulate_times)
uct = wins / simulate_times + math.sqrt(
2 * math.log(s_parent) / simulate_times)
if uct > best_uct:
best_move = move
index = randint(0, len(moves) - 1)
move = moves[index]
return move
def simulate(self, move, simulate_times):
win = 0
self.board.make_move(move, self.color)
for _ in range(simulate_times):
curr_turn = self.opponent[self.color]
t = 0
moves = self.get_moves(self.board, curr_turn)
while len(moves) > 0 and t < 50:
move = self.rollout(moves)
self.board.make_move(move, curr_turn)
curr_turn = self.opponent[curr_turn]
moves = self.get_moves(self.board, curr_turn)
t += 1
win += 1 if curr_turn != self.color else 0 if t != 50 else 0.5
self.undo(self.board, t)
print(win / simulate_times * 100)
bf, wf = self.board_to_feature(self.board, self.color)
self.write_to_file(bf, wf, win / simulate_times * 100)
self.board.undo()
return win
def board_to_feature(self, board, color):
# result = ""
# result += f"{board.white_count/self.total} {board.black_count/self.total} "
#
# wking,bking = self.wking_bking(board)
# result += f"{wking/self.total} {bking/self.total} "
#
# wback, bback = self.wback_bback(board)
# result += f"{wback/self.total} {bback/self.total} "
#
# wedge, bedge = self.wedge_bedge(board)
# result += f"{wedge/self.total} {bedge/self.total} "
#
# wdiagonal, bdiagonal = self.wdiagonal_bdiagonal(board)
# result += f"{wdiagonal/self.total} {bdiagonal/self.total} "
#
# wdis, bdis = self.wdis_bdis(board)
# result += f"{wdis/self.total} {bdis/self.total} "
#
# result += str(self.movecount)
wking, bking = self.wking_bking(board)
wcount, bcount = self.wcount_bcount(board)
wdis, bdis = self.wdis_bdis(board)
wedge, bedge = self.wedge_bedge(board)
wcenter, bcenter = self.wcenter_bcenter(board)
wback, bback = self.wback_bback(board)
wdiag, bdiag = self.wdiag_bdiag(board)
wdog, bdog = self.wdog_bdog(board)
wbridge, bbridge = self.wbridge_bbridge(board)
wuptriangle, buptriangle = self.wuptriangle_buptriangle(board)
wdowntriangle, bdowntriangle = self.wdowntriangle_bdowntriangle(board)
woreo, boreo = self.woreo_boreo(board)
if self.color == 1:
wmoveable, weatable = self.moveables(board, 2)
bmoveable, beatable = 0, 0
else:
wmoveable, weatable = 0, 0
bmoveable, beatable = self.moveables(board, 1)
return [wcount, wking, wdis, wback, wedge,
wcenter, wdiag, wdog, wbridge, wuptriangle,
wdowntriangle, woreo, wmoveable, weatable],\
[bcount, bking, bdis, bback, bedge,
bcenter, bdiag, bdog, bbridge, buptriangle,
bdowntriangle, boreo, bmoveable, beatable]
def wcount_bcount(self, board):
return board.white_count, board.black_count
def wking_bking(self, board):
bking, wking = 0, 0
for r in range(self.board.row):
for c in range(self.board.col):
if self.board.board[r][c].color == "B":
bking += self.board.board[r][c].is_king
elif self.board.board[r][c].color == "W":
wking += self.board.board[r][c].is_king
return wking, bking
def moveables(self, board, color):
moves = [
m for chess in board.get_all_possible_moves(color) for m in chess
]
eatable = 0
for m in moves:
if len(m.seq) > 2:
eatable += (len(m.seq) - 1)
continue
if math.sqrt((m.seq[0][0] - m.seq[1][0])**2 +
(m.seq[0][1] - m.seq[1][1])**2) > 1:
eatable += 1
# print(f"len(moves): {len(moves)}, eatable: {eatable}")
return len(moves), eatable
def wback_bback(self, board):
bback = sum(board.board[0][i].color == "B" for i in range(board.col))
wback = sum(board.board[board.row - 1][i].color == "W"
for i in range(board.col))
return wback, bback
def wedge_bedge(self, board):
bedge = sum((board.board[i][0].color == "B") +
(board.board[i][board.col - 1].color == "B")
for i in range(board.row))
wedge = sum((board.board[i][0].color == "W") +
(board.board[i][board.col - 1].color == "W")
for i in range(board.row))
# print(f"wedge: {wedge}, bedge: {bedge}")
return wedge, bedge
def wcenter_bcenter(self, board):
wcenter = sum((board.board[int(board.row/2)][i].color =="W")+ \
(board.board[int(board.row/2)+1][i].color =="W") for i in range(board.col))
bcenter = sum((board.board[int(board.row/2)][i].color == "B")+ \
(board.board[int(board.row/2)+1][i].color =="B") for i in range(board.col))
# print(f"wcenter: {wcenter}, bcenter: {bcenter}")
return wcenter, bcenter
def wdiagonal_bdiagonal(self, board):
bdiagonal = sum(board.board[i][i].color == "B" for i in range(board.row//4, 3*board.row//4)) + \
sum(board.board[board.row - 1 - i][board.row - 1 - i].color == "B" for i in range(board.row))
wdiagonal = sum(board.board[i][i].color == "W" for i in range(board.row)) + \
sum(board.board[board.row - 1 - i][board.row - 1 - i].color == "W" for i in range(board.row))
# print(f"wdiagonal: {wdiagonal}, bdiagonal: {bdiagonal}")
return wdiagonal, bdiagonal
def wdiag_bdiag(self, board):
bc, wc = 0, 0
for r in range(board.row - 1):
bc += (board.board[r][r].color == "B") + (board.board[r+1][r].color == "B") + (board.board[r][r+1].color == "B") \
+ (board.board[r][board.col-1-r].color == "B") + (board.board[r+1][board.col-1-r].color == "B") +\
(board.board[r][board.col-2-r].color == "B")
wc += (board.board[r][r].color == "W") + (board.board[r + 1][r].color == "W") + (board.board[r][r + 1].color == "W")\
+ (board.board[r][board.col-1-r].color == "W") + (board.board[r+1][board.col-1-r].color == "W") +\
(board.board[r][board.col-2-r].color == "W")
bc += (board.board[board.row - 1][0].color == "B") + (
board.board[board.row - 1][board.row - 1].color == "B")
wc += (board.board[board.row - 1][0].color == "W") + (
board.board[board.row - 1][board.row - 1].color == "W")
# print(f"wdiag: {wc}, bdiag: {bc}")
return wc, bc
def wdog_bdog(self, board):
wc = (board.board[board.row-1][board.col-1].color == "." and board.board[board.row-1][board.col-2].color == "W" \
and board.board[board.row-2][board.col-1].color == "B") +\
(board.board[board.row-1][0].color == "." and board.board[board.row-1][1].color == "W"\
and board.board[board.row-2][0].color == "B")
bc = (board.board[0][0].color == "." and board.board[0][1].color == "B" \
and board.board[1][0].color == "W") + \
(board.board[0][board.col-1].color == "." and board.board[0][board.col-2].color == "B" \
and board.board[1][board.col-1].color == "W")
# print(f"wdog: {wc}, bdog: {bc}")
return wc, bc
def wbridge_bbridge(self, board):
bc = sum(
board.board[0][c].color == "B" and board.board[0][c +
2].color == "B"
for c in range(1, board.col - 3))
wc = sum(board.board[board.row - 1][c].color == "W"
and board.board[board.row - 1][c + 2].color == "W"
for c in range(1, board.col - 3))
# print(f"wbridge: {wc}, bbridge: {bc}")
return wc, bc
def wuptriangle_buptriangle(self, board):
bcount, wcount = 0, 0
for r in range(1, board.row - 1):
for c in range(board.col - 2):
if board.board[r][c].color == "B" and board.board[r - 1][
c + 1].color == "B" and board.board[r][c +
2].color == "B":
bcount += 1
if board.board[r][c].color == "W" and board.board[r - 1][
c + 1].color == "W" and board.board[r][c +
2].color == "W":
wcount += 1
# print(f"wuptriangle: {wcount}, buptriangle: {bcount}")
return wcount, bcount
def wdowntriangle_bdowntriangle(self, board):
bcount, wcount = 0, 0
for r in range(board.row - 1):
for c in range(board.col - 2):
if board.board[r][c].color == "B" and board.board[r + 1][
c + 1].color == "B" and board.board[r][c +
2].color == "B":
bcount += 1
if board.board[r][c].color == "W" and board.board[r + 1][
c + 1].color == "W" and board.board[r][c +
2].color == "W":
wcount += 1
# print(f"wdowntriangle: {wcount}, bdowntriangle: {bcount}")
return wcount, bcount
def woreo_boreo(self, board):
'''
:param board:
:return: triangle pattern in the last row
'''
boreo = sum(board.board[0][c].color == "B" and board.board[1][c+1].color == "B" \
and board.board[0][c+2].color == "B" for c in range(0, board.col-2))
woreo = sum(board.board[board.row-1][c].color == "W" and board.board[board.row-2][c+1].color == "W" \
and board.board[board.row-1][c+2].color == "W" for c in range(0, board.col-2))
# print(f"woreo: {woreo}, boreo: {boreo}")
return woreo, boreo
def wdis_bdis(self, board):
wdis = sum(board.row - 1 - i for i in range(board.row)
for j in range(board.col) if board.board[i][j].color == "W")
bdis = sum(i for i in range(board.row) for j in range(board.col)
if board.board[i][j].color == "B")
return wdis, bdis
######### help function #########
def rollout(self, moves):
'''Random roll a move from moves'''
return moves[randint(0, len(moves) - 1)]
def get_moves(self, board, turn):
return [
m for chess in board.get_all_possible_moves(turn) for m in chess
]
def undo(self, board, times):
for _ in range(times):
board.undo()
def write_to_file(self, wfeatures, bfeatures, win_rate):
with open(self.file, "a") as f:
w = ' '.join(str(x) for x in wfeatures)
b = ' '.join(str(x) for x in bfeatures)
f.write(w + ' ' + b + ' ' + str(win_rate) + '\n')
class StudentAI():
def __init__(self,col,row,p):
self.col = col
self.row = row
self.p = p
self.board = Board(col,row,p)
self.board.initialize_game()
self.color = ''
self.opponent = {1:2,2:1}
self.color = 2
def get_move(self,move):
if len(move) != 0:
self.board.make_move(move,self.opponent[self.color])
else:
self.color = 1
moves = self.board.get_all_possible_moves(self.color)
index = randint(0,len(moves)-1)
inner_index = randint(0,len(moves[index])-1)
# move = moves[index][inner_index]
move = self.min_max_recursion(4, True)[0]
self.board.make_move(move,self.color)
return move
def min_max_recursion(self, depth, maximizingPlayer):
if depth == 0 and self.color == 1:
return self.board.black_count - self.board.white_count
elif depth == 0 and self.color == 2:
return self.board.white_count - self.board.black_count
maximum = -100
max_move = ""
minimum = 100
min_move = ""
if maximizingPlayer:
selfmoves = self.board.get_all_possible_moves(self.color)
#maximum = -100
for s_checker_moves in selfmoves:
for sm in s_checker_moves:
self.board.make_move(sm, self.color)
Recurs = self.min_max_recursion(depth - 1, False)
# print("Recurs: ",Recurs)
temp = maximum
if type(Recurs) == type(tuple()):
maximum = max(maximum, Recurs[1])
else:
maximum = max(maximum, Recurs)
# print("maximum: ",maximum)
if temp != maximum:
max_move = sm
#alpha = max(alpha, Recurs)
# print("alpha",alpha)
self.board.undo()
#if beta <= alpha:
# break
return (max_move, maximum)
else:
#minimum = 100
oppmoves = self.board.get_all_possible_moves(self.opponent[self.color])
for o_checker_moves in oppmoves:
for om in o_checker_moves:
self.board.make_move(om, self.opponent[self.color])
Recurs = self.min_max_recursion(depth - 1, True)
# print("Recurs: ",Recurs)
temp = minimum
if type(Recurs) == type(tuple()):
minimum = min(minimum, Recurs[1])
else:
minimum = min(minimum, Recurs)
# print("minimum: ",minimum)
if temp != minimum:
min_move = om
#beta = min(beta, Recurs)
# print("beta: ", beta)
self.board.undo()
#if beta <= alpha:
# break
return (min_move, minimum)
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
self.movecount = 1
self.file = f"{self.col}-{self.row}-{self.color}-{randint(0, 500)}-test.txt"
self.start = time.time()
self.theta1, self.theta2 = self.get_theta()
self.cutoff = self.get_cutoff()
# self.theta = [8.61043154e+00, 4.48291855e+00, 7.78473553e+00, -7.07767178e-14,2.06230092e+00, 1.18768964e+00]#, 0]
# self.theta = [-24.13, -7.87, -17.89, -16.67, -6.99, 7.22, 1.19, 0.72,
# -4.2, -4.52, -2.49, -3.14, 5.69, 0.02, 3.53, -3.58, 9.37,
# -3.81, -1.58, -1.75, 2.51, 0.26, 18.3, 10.25, 3.63,
# 3.69, 1.32, -4.03]
# self.theta = [-57.35, -6.41, -2.09, -38.9, -3.91, 6.48, 11.97, -0.39, 27.23, 11.11, -22.04, -11.36, 39.62, -41.32,
# 55.17, 24.54, 16.05, 12.08, 10.46, -17.8, 5.61, -7.38, 48.46, 20.26, 4.3, 2.54, 0.0, 0.0]
# self.theta77 = [-1.49, 0.41, 0.0, -0.19, -0.07, 0.25, 0.13, 0.0, 0.0, 0.09, -0.28, -0.53, 3.83, -3.95,
# 1.88, 0.93, 0.08, 0.25, 0.17, 0.0, -0.22, 0.0, -0.24, -0.2, -0.02, 0.03, 0.0, 0.0]
# self.theta98 = [-1.76, -0.4, 0.03, -0.08, 0.16, 0.3, 0.16, 0.55, -0.38, -0.17, -0.12, 0.28, 2.8, -2.77,
# 1.82, 0.82, 0.22, 0.1, 0.09, -0.38, -0.09, -1.31, 0.78, 0.42, -0.02, 0.15, 0.0, 0.0]
def get_move(self, move):
print(self.color)
self.time = time.time()
# if self.time - self.start > 400:
# self.depth = 4
if len(move) != 0:
self.board.make_move(move, self.opponent[self.color])
else:
self.color = 1
moves = self.board.get_all_possible_moves(self.color)
self.depth = self.get_depth()
# index = randint(0,len(moves)-1)
# inner_index = randint(0,len(moves[index])-1)
# move = moves[index][inner_index]
# print(moves)
move = moves[0][0] if len(moves) == 1 and len(
moves[0]) == 1 else self.minimax_move(moves)
self.board.make_move(move, self.color)
self.movecount += 1
# with open(self.file, 'a') as f:
# f.write(f"Movecount:{self.movecount} Total time:{time.time()-self.start} This move takes:{time.time()-self.time} Depth:{self.depth}\n")
return move
def get_cutoff(self):
if self.row == 7 and self.col == 7:
return (5, 3)
else:
return (6, 3)
def get_depth(self):
if self.row == 7 and self.col == 7:
return 0
else:
return 4
def get_theta(self):
theta771_start = [
-3.05, -1.9, 0.04, -0.85, -0.06, 0.43, 0.21, 0.0, -0.18, 0.56,
-0.32, -0.25, 2.9, -2.56, 1.23, 0.06, 0.44, 0.89, 0.67, 0.27, -0.0,
0.0, 0.64, 0.56, 0.07, 0.91, 0.0, 0.0
]
theta771_mid = [
-2.84, -1.09, -0.05, -0.67, 0.22, 0.42, 0.24, 0.0, -0.18, 0.72,
0.26, 0.38, 3.44, -2.88, 1.83, 0.04, 0.3, 0.79, 0.11, -0.02, -0.14,
0.0, 0.36, 0.3, -0.45, 0.9, 0.0, 0.0
]
theta771_last = [
-3.01, -1.01, -0.06, 0.13, 0.42, 0.18, 0.03, 0.0, -0.51, 0.75,
1.31, 1.26, 2.86, -2.55, 1.5, 0.22, 0.05, 0.42, 0.2, -0.06, -0.16,
0.0, 0.53, 0.45, -0.26, 0.44, 0.0, 0.0
]
theta772_start = [
2.07, -0.44, 0.29, 0.45, 0.32, -0.46, -0.04, 0.0, 0.37, -0.25,
0.16, 0.17, 0.0, 0.0, -2.18, -0.62, -0.16, -1.0, 0.24, 0.72, 0.2,
0.0, 0.12, 0.04, 0.75, -1.29, 3.36, -2.93
]
theta772_mid = [
1.93, 0.09, 0.16, 0.37, 0.28, -0.18, -0.17, 0.0, 0.02, -0.28, 0.02,
0.13, 0.0, 0.0, -2.3, -1.12, -0.07, -0.63, 0.38, 0.08, 0.16, 0.0,
-0.4, 0.43, 0.54, -0.74, 3.47, -3.05
]
theta772_last = [
1.51, 0.44, -0.01, 0.11, 0.16, -0.16, -0.14, 0.0, -0.17, 0.47,
-0.16, 0.02, 0.0, 0.0, -2.79, -0.98, 0.0, 0.14, 0.4, -0.08, 0.0,
0.0, -0.26, 1.25, 0.06, -0.07, 2.32, -2.19
]
theta981_start = [
-2.85, -0.22, 0.11, -0.54, 0.31, -0.03, 0.07, 0.0, -0.19, 0.45,
-0.17, 0.45, 2.09, -1.88, 2.46, 1.24, -0.04, 0.64, 0.41, -0.36,
0.11, 0.25, -0.01, 0.09, -0.34, 0.96, 0.0, 0.0
]
theta981_mid = [
-1.95, -1.35, 0.11, -0.44, 0.11, 0.16, 0.02, 0.0, -0.18, 0.57,
0.26, 0.54, 2.37, -2.06, 1.22, -0.19, 0.3, 0.9, 0.43, 0.09, 0.05,
-0.06, 0.19, 0.19, -0.04, 0.69, 0.0, 0.0
]
theta981_last = [
-3.22, -1.85, 0.18, 0.48, 0.23, 0.14, -0.18, 0.0, -0.53, 1.25,
0.93, 0.03, 3.33, -3.01, 2.08, 0.05, 0.15, 0.65, 0.27, 0.03, -0.13,
-1.0, -0.24, -0.25, 0.01, 0.03, 0.0, 0.0
]
theta982_start = [
1.47, -0.0, 0.21, 0.5, 0.09, -0.09, 0.05, 0.0, 0.12, -0.45, 0.15,
0.55, 0.0, 0.0, -2.5, -0.49, 0.05, -0.45, 0.24, 0.24, 0.29, 0.29,
0.02, 0.12, 0.26, -0.26, 2.31, -2.11
]
theta982_mid = [
1.22, -0.16, 0.29, 0.22, 0.28, -0.21, -0.05, 0.0, 0.38, -0.24,
-0.31, 0.59, 0.0, 0.0, -2.04, -1.22, 0.14, -0.54, 0.12, -0.07,
0.05, -0.04, 0.31, 0.57, 0.41, -0.88, 3.14, -2.84
]
theta982_last = [
2.12, -0.19, 0.19, -0.23, 0.33, -0.17, -0.11, 0.0, -0.44, 0.32,
0.16, 0.0, 0.0, 0.0, -2.06, -1.58, 0.09, -0.16, 0.46, -0.19, 0.0,
-0.94, -0.7, 0.74, 1.33, -0.08, 4.14, -3.98
]
if self.row == 7 and self.col == 7:
return (theta771_start, theta771_mid,
theta771_last), (theta772_start, theta772_mid,
theta772_last)
else:
return (theta981_start, theta981_mid,
theta981_last), (theta982_start, theta982_mid,
theta982_last)
def minimax_move(self, moves: [list]):
best = []
max_value = -math.inf
for chess in moves:
for move in chess:
val = self.min_value(move, self.depth, -math.inf, math.inf)
if val > max_value:
best = [move]
max_value = val
elif val == max_value:
best.append(move)
return best[0]
def min_value(self, move, depth, alpha, beta):
self.board.make_move(move, self.color)
if depth == 0:
u = self.utility(self.board, self.color)
print(u)
self.board.undo()
return u
moves = self.board.get_all_possible_moves(self.opponent[self.color])
moves = [m for sub in moves for m in sub]
# moves = self.reorder(self.get_u_list(moves, self.board, self.opponent[self.color]), reverse = False)
if len(moves) == 0:
u = +1000
self.board.undo()
return u
min_val = math.inf
for move in moves:
min_val = min(self.max_value(move, depth - 1, alpha, beta),
min_val)
beta = min(beta, min_val)
if alpha >= beta:
self.board.undo()
return min_val
self.board.undo()
return min_val
def max_value(self, move, depth, alpha, beta):
self.board.make_move(move, self.opponent[self.color])
if depth == 0:
u = self.utility(self.board, self.opponent[self.color])
print(u)
self.board.undo()
return u
moves = self.board.get_all_possible_moves(self.color)
moves = [m for sub in moves for m in sub]
# moves = self.reorder(self.get_u_list(moves, self.board, self.color), reverse = True)
if len(moves) == 0:
u = -1000
self.board.undo()
return u
max_val = -math.inf
for move in moves:
max_val = max(self.min_value(move, depth - 1, alpha, beta),
max_val)
alpha = max(alpha, max_val)
if alpha >= beta:
self.board.undo()
return max_val
self.board.undo()
return max_val
def u_after_move(self, move, board, color):
board.make_move(move, color)
u = self.utility(board, color)
board.undo()
return u
def get_u_list(self, moves, board, color):
u_list = {}
for chess in moves:
for move in chess:
u_list[move] = self.u_after_move(move, board, color)
return u_list
def reorder(self, u_list, reverse):
return sorted(u_list, key=lambda x: u_list[x], reverse=reverse)
def utility(self, board, color):
wking, bking = self.wking_bking(board)
wcount, bcount = self.wcount_bcount(board)
wdis, bdis = self.wdis_bdis(board)
wedge, bedge = self.wedge_bedge(board)
wcenter, bcenter = self.wcenter_bcenter(board)
wback, bback = self.wback_bback(board)
wdiag, bdiag = self.wdiag_bdiag(board)
wdog, bdog = self.wdog_bdog(board)
wbridge, bbridge = self.wbridge_bbridge(board)
wuptriangle, buptriangle = self.wuptriangle_buptriangle(board)
wdowntriangle, bdowntriangle = self.wdowntriangle_bdowntriangle(board)
woreo, boreo = self.woreo_boreo(board)
board.show_board()
if color == 1:
wmoveable, weatable = self.moveables(board, 2)
bmoveable, beatable = 0, 0
else:
wmoveable, weatable = 0, 0
bmoveable, beatable = self.moveables(board, 1)
if self.color == 1:
features = [
wcount, wking, wdis, wback, wedge, wcenter, wdiag, wdog,
wbridge, wuptriangle, wdowntriangle, woreo, wmoveable,
weatable, bcount, bking, bdis, bback, bedge, bcenter, bdiag,
bdog, bbridge, buptriangle, bdowntriangle, boreo, bmoveable,
beatable
]
print(str([i for i in features]))
if bcount > self.cutoff[0]:
return sum(x * t for x, t in zip(features, self.theta1[0]))
elif self.cutoff[1] < bcount <= self.cutoff[0]:
return sum(x * t for x, t in zip(features, self.theta1[1]))
else:
return sum(x * t for x, t in zip(features, self.theta1[2]))
else:
features = [
wcount, wking, wdis, wback, wedge, wcenter, wdiag, wdog,
wbridge, wuptriangle, wdowntriangle, woreo, wmoveable,
weatable, bcount, bking, bdis, bback, bedge, bcenter, bdiag,
bdog, bbridge, buptriangle, bdowntriangle, boreo, bmoveable,
beatable
]
print(str([i for i in features]))
if wcount > self.cutoff[0]:
return sum(x * t for x, t in zip(features, self.theta2[0]))
elif self.cutoff[1] < wcount <= self.cutoff[0]:
return sum(x * t for x, t in zip(features, self.theta2[1]))
else:
return sum(x * t for x, t in zip(features, self.theta2[2]))
def features(self, board, color):
'''
:param board:
:return: white_features, black_features
features order = [count, king, dis, back, edge,
center, diag, dog, bridge, uptriangle,
downtriangle, oreo, moveable, eatable]
'''
wfeature = [0 for _ in range(14)]
bfeature = [0 for _ in range(14)]
wfeature[0], bfeature[0] = board.white_count, board.black_count
for r in range(board.row):
# count edge
wfeature[4] += (board.board[r][0].color == "W") + (
board.board[r][board.col - 1].color == "W")
bfeature[4] += (board.board[r][0].color == "B") + (
board.board[r][board.col - 1].color == "B")
for c in range(board.col):
wfeature[3] += (board.board[board.row - 1][c].color == "B"
) # count back
bfeature[3] += (board.board[0][c].color == "B") # count back
wfeature[5] += (board.board[int(
board.row / 2)][c].color == "W") + (
board.board[int(board.row / 2) + 1][c].color == "W"
) # count center
bfeature[5] += (board.board[int(
board.row / 2)][c].color == "B") + (
board.board[int(board.row / 2) + 1][c].color == "B"
) # count center
if board.board[r][c].color == 'W':
if board.board[r][c].is_king:
wfeature[1] += 1 # count king
wfeature[2] += board.row - 1 - r # count dis
elif board.board[r][c].color == 'B':
if board.board[r][c].is_king:
bfeature[1] += 1 # count king
bfeature[2] += r # count dis
def wcount_bcount(self, board):
return board.white_count, board.black_count
def wking_bking(self, board):
bking, wking = 0, 0
for r in range(self.board.row):
for c in range(self.board.col):
if self.board.board[r][c].color == "B":
bking += self.board.board[r][c].is_king
elif self.board.board[r][c].color == "W":
wking += self.board.board[r][c].is_king
return wking, bking
def moveables(self, board, color):
moves = [
m for chess in board.get_all_possible_moves(color) for m in chess
]
eatable = 0
for m in moves:
if len(m.seq) > 2:
eatable += (len(m.seq) - 1)
continue
if math.sqrt((m.seq[0][0] - m.seq[1][0])**2 +
(m.seq[0][1] - m.seq[1][1])**2) > 1:
eatable += 1
# print(f"len(moves): {len(moves)}, eatable: {eatable}")
return len(moves), eatable
def wback_bback(self, board):
bback = sum(board.board[0][i].color == "B" for i in range(board.col))
wback = sum(board.board[board.row - 1][i].color == "W"
for i in range(board.col))
return wback, bback
def wedge_bedge(self, board):
bedge = sum((board.board[i][0].color == "B") +
(board.board[i][board.col - 1].color == "B")
for i in range(board.row))
wedge = sum((board.board[i][0].color == "W") +
(board.board[i][board.col - 1].color == "W")
for i in range(board.row))
# print(f"wedge: {wedge}, bedge: {bedge}")
return wedge, bedge
def wcenter_bcenter(self, board):
wcenter = sum((board.board[int(board.row / 2)][i].color == "W") + \
(board.board[int(board.row / 2) + 1][i].color == "W") for i in range(board.col))
bcenter = sum((board.board[int(board.row / 2)][i].color == "B") + \
(board.board[int(board.row / 2) + 1][i].color == "B") for i in range(board.col))
# print(f"wcenter: {wcenter}, bcenter: {bcenter}")
return wcenter, bcenter
def wdiagonal_bdiagonal(self, board):
bdiagonal = sum(board.board[i][i].color == "B" for i in range(board.row // 4, 3 * board.row // 4)) + \
sum(board.board[board.row - 1 - i][board.row - 1 - i].color == "B" for i in range(board.row))
wdiagonal = sum(board.board[i][i].color == "W" for i in range(board.row)) + \
sum(board.board[board.row - 1 - i][board.row - 1 - i].color == "W" for i in range(board.row))
# print(f"wdiagonal: {wdiagonal}, bdiagonal: {bdiagonal}")
return wdiagonal, bdiagonal
def wdiag_bdiag(self, board):
bc, wc = 0, 0
for r in range(board.row - 1):
bc += (board.board[r][r].color == "B") + (board.board[r + 1][r].color == "B") + (
board.board[r][r + 1].color == "B") \
+ (board.board[r][board.col - 1 - r].color == "B") + (
board.board[r + 1][board.col - 1 - r].color == "B") + \
(board.board[r][board.col - 2 - r].color == "B")
wc += (board.board[r][r].color == "W") + (board.board[r + 1][r].color == "W") + (
board.board[r][r + 1].color == "W") \
+ (board.board[r][board.col - 1 - r].color == "W") + (
board.board[r + 1][board.col - 1 - r].color == "W") + \
(board.board[r][board.col - 2 - r].color == "W")
bc += (board.board[board.row - 1][0].color == "B") + (
board.board[board.row - 1][board.row - 1].color == "B")
wc += (board.board[board.row - 1][0].color == "W") + (
board.board[board.row - 1][board.row - 1].color == "W")
# print(f"wdiag: {wc}, bdiag: {bc}")
return wc, bc
def wdog_bdog(self, board):
wc = (board.board[board.row - 1][board.col - 1].color == "." and board.board[board.row - 1][
board.col - 2].color == "W" \
and board.board[board.row - 2][board.col - 1].color == "B") + \
(board.board[board.row - 1][0].color == "." and board.board[board.row - 1][1].color == "W" \
and board.board[board.row - 2][0].color == "B")
bc = (board.board[0][0].color == "." and board.board[0][1].color == "B" \
and board.board[1][0].color == "W") + \
(board.board[0][board.col - 1].color == "." and board.board[0][board.col - 2].color == "B" \
and board.board[1][board.col - 1].color == "W")
# print(f"wdog: {wc}, bdog: {bc}")
return wc, bc
def wbridge_bbridge(self, board):
bc = sum(
board.board[0][c].color == "B" and board.board[0][c +
2].color == "B"
for c in range(1, board.col - 3))
wc = sum(board.board[board.row - 1][c].color == "W"
and board.board[board.row - 1][c + 2].color == "W"
for c in range(1, board.col - 3))
# print(f"wbridge: {wc}, bbridge: {bc}")
return wc, bc
def wuptriangle_buptriangle(self, board):
bcount, wcount = 0, 0
for r in range(1, board.row - 1):
for c in range(board.col - 2):
if board.board[r][c].color == "B" and board.board[r - 1][
c + 1].color == "B" and board.board[r][c +
2].color == "B":
bcount += 1
if board.board[r][c].color == "W" and board.board[r - 1][
c + 1].color == "W" and board.board[r][c +
2].color == "W":
wcount += 1
# print(f"wuptriangle: {wcount}, buptriangle: {bcount}")
return wcount, bcount
def wdowntriangle_bdowntriangle(self, board):
bcount, wcount = 0, 0
for r in range(board.row - 1):
for c in range(board.col - 2):
if board.board[r][c].color == "B" and board.board[r + 1][
c + 1].color == "B" and board.board[r][c +
2].color == "B":
bcount += 1
if board.board[r][c].color == "W" and board.board[r + 1][
c + 1].color == "W" and board.board[r][c +
2].color == "W":
wcount += 1
# print(f"wdowntriangle: {wcount}, bdowntriangle: {bcount}")
return wcount, bcount
def woreo_boreo(self, board):
'''
:param board:
:return: triangle pattern in the last row
'''
boreo = sum(board.board[0][c].color == "B" and board.board[1][c + 1].color == "B" \
and board.board[0][c + 2].color == "B" for c in range(0, board.col - 2))
woreo = sum(board.board[board.row - 1][c].color == "W" and board.board[board.row - 2][c + 1].color == "W" \
and board.board[board.row - 1][c + 2].color == "W" for c in range(0, board.col - 2))
# print(f"woreo: {woreo}, boreo: {boreo}")
return woreo, boreo
def wdis_bdis(self, board):
wdis = sum(board.row - 1 - i for i in range(board.row)
for j in range(board.col) if board.board[i][j].color == "W")
bdis = sum(i for i in range(board.row) for j in range(board.col)
if board.board[i][j].color == "B")
return wdis, bdis
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
#---------------------------------#
self.area = self.row * self.col
self.count = 0
if self.area <= 39:
self.depth = 8
elif self.area <= 49:
self.depth = 5
elif self.area <= 79:
self.depth = 4
else:
self.depth = 4
#get move of current game state
def get_move(self, move):
if len(move) != 0:
self.board.make_move(move, self.opponent[self.color])
else:
self.color = 1
#----------------------------------------------------------#
# MINIMAX GAME TREE SEARCH AGENT
#----------------------------------------------------------#
root = MinimaxTree(self.opponent[self.color])
self.recursive_dfs(root, self.depth)
self.recursive_minimax(root)
move_list = root.data[list(root.data)[0]]
current = move_list[0]
#self.board.make_move(current, self.color)
best_mcts_move = current
move = current
#----------------------------------------------------------#
'''
#returns some random move from the list
#--- DEBUGGING PURPOSES ---
moves_user = self.board.get_all_possible_moves(self.color)
moves_opponent = self.board.get_all_possible_moves(self.opponent[self.color])
print("---USER MOVES---")
for item in moves_user:
for i in item:
print(i.seq)
print("---OPPONENT MOVES---")
for item in moves_opponent:
for i in item:
print(i.seq)
board_sim = copy.deepcopy(self.board)
move_sim = moves_user[0]
print("Simulation Making move:" + str(move_sim))
board_sim.make_move(move_sim[0], self.color)
print("----- SIMULATION B -----")
board_sim.show_board()
print("Simulation score =" + str(self.board_heuristic(board_sim)))
print("Terminal? :" + str(board_sim.is_win(self.color)))
print("----------------------")
#current2 = Move(moves_user[randint(0, len(moves_user) - 1)])
#self.board.make_move(list(current2[0]), self.color)
#print(current2)
print("AI Making Move:" + str(move))'''
#----------------------------------------------------------#
# MONTE CARLO TREE SEARCH AGENT
#----------------------------------------------------------#
m_list = self.board.get_all_possible_moves(self.color)
if len(m_list) == 1:
self.board.make_move(m_list[0][0], self.color)
return m_list[0][0]
root = MonteCarloTree(self.board, self.color, m_list)
self.board.make_move(best_mcts_move, self.color)
move = best_mcts_move
#----------------------------------------------------------#
return move
# -------- CHECKERS BOARD HEURISTIC FUNCTION --------#
#Sources Referred:
#https://github.com/techwithtim/Python-Checkers-AI/blob/master/checkers/board.py
#https://www.cs.huji.ac.il/~ai/projects/old/English-Draughts.pd
@staticmethod
def check_distance(p1, p2):
#for two given checker pieces return the distance
#using the distance formula sqrt((x2-x1)^2 + (y2-y1)^2)
dist = sqrt(((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2))
return dist
def board_heuristic(self, board):
score = 0
king_score = 10 + (self.col - 1)
pawns_b = [] #black pawns
pawns_w = [] #white pawns
kings_b = [] #black kings
kings_w = [] #white kings
for r in range(self.row):
for c in range(self.col):
piece = board.board[r][c]
if piece.color == "B":
if piece.is_king:
kings_b.append((r, c))
else:
pawns_b.append((r, c))
elif piece.color == "W":
if piece.is_king:
kings_w.append((r, c))
else:
pawns_w.append((r, c))
for pb in pawns_b:
score = score + pb[0] + 10
for pw in pawns_w:
score = score - (10 + (self.row - 1 - pw[0]))
for kb in kings_b:
score = score + king_score
distance = 0
for kw in kings_w:
distance = distance + self.check_distance(kb, kw)
for pw in pawns_w:
distance = distance + self.check_distance(kb, pw)
if len(kings_w) + len(pawns_w) != 0:
score = score - (distance / (len(kings_w) + len(pawns_w)))
for kw in kings_w:
score = score + king_score
distance = 0
for kb in kings_b:
distance = distance + self.check_distance(kw, kb)
for pb in pawns_b:
distance = distance + self.check_distance(kw, pb)
if len(kings_b) + len(pawns_b) != 0:
score = score - (distance / (len(kings_b) + len(pawns_b)))
if self.color == 2:
return score
else:
return -score
#Sources Referred:
#https://www.geeksforgeeks.org/depth-first-search-or-dfs-for-a-graph/
def recursive_dfs(self, root: MinimaxTree, depth=1):
if depth == 0:
pass
else:
if root.move is not None:
self.board.make_move(root.move, root.color)
all_moves_list = self.board.get_all_possible_moves(
self.opponent[root.color])
for r in range(len(all_moves_list)):
for c in range(len(all_moves_list[r])):
root.child_nodes.append(
MinimaxTree(self.opponent[root.color],
all_moves_list[r][c]))
for node in root.child_nodes:
self.recursive_dfs(node, depth - 1)
if root.move is not None:
self.board.undo()
#-------- MINIMAX ALGORITHM-------#
#Sources Referred:
#https://www.geeksforgeeks.org/minimax-algorithm-in-game-theory-set-1-introduction/?ref=lbp
#https://www.geeksforgeeks.org/minimax-algorithm-in-game-theory-set-4-alpha-beta-pruning/
#https://github.com/techwithtim/Python-Checkers-AI/blob/master/minimax/algorithm.py
def min_or_max(self, color):
if color == self.color:
return max
else:
return min
def minimax(self, color, children):
min_or_max = self.min_or_max(color)
hash_table = {}
for child in children:
for val in child.data.keys():
hash_table.setdefault(val, []).append(child.move)
return {min_or_max(hash_table): hash_table[min_or_max(hash_table)]}
def recursive_minimax(self, root: MinimaxTree):
if root.move is not None:
self.board.make_move(root.move, root.color)
if len(root.child_nodes) == 0:
root.data = {self.board_heuristic(self.board): []}
else:
for node in root.child_nodes:
self.recursive_minimax(node)
root.data = self.minimax(root.color, root.child_nodes)
if root.move is not None:
self.board.undo()
# -------- MONTE CARLO TREE SEARCH ALGORITHM-------#
# Sources Referred:
# https://www.geeksforgeeks.org/ml-monte-carlo-tree-search-mcts/
# https://int8.io/monte-carlo-tree-search-beginners-guide/
# https://www.analyticsvidhya.com/blog/2019/01/monte-carlo-tree-search-introduction-algorithm-deepmind-alphago/
def simulate(self, board, move):
#print("SIMULATE")
#board_sim = copy.deepcopy(board)
#board_sim.make_move(move, self.color)
board.make_move(move, self.color)
score = self.board_heuristic(board) #board_sim
return score, board #,board_sim
def expansion(self, node: MonteCarloTree):
#print("EXPANSION")
#print(node)
if node.board.is_win(self.color) > 0 or node.board.is_win(
self.opponent[self.color]) > 0:
node.expanded = True
return
for move_set in node.move_list:
for move in move_set:
score, new_board = self.simulate(node.board, move)
m_list = new_board.get_all_possible_moves(self.color)
new_mcts_node = MonteCarloTree(new_board, self.color, m_list)
new_mcts_node.move = move
new_mcts_node.parent_node = node
new_mcts_node.board_eval = score
node.child_nodes.append(new_mcts_node)
node.board.undo()
#print("FINAL EXPANSION:")
#print(node)
def rollout(self, node: MonteCarloTree, steps=0):
#print("ROLLOUT")
#print(node)
if node.board.is_win(self.color) > 0 or node.board.is_win(
self.opponent[self.color]) > 0: #or steps > 1000:
node.no_of_wins += int(node.board.is_win(self.color))
node.no_of_steps = steps
#print("Roll-BACKPROPOGATE")
self.backpropogate(node)
return
while node.board.is_win(self.color) < 1 or node.board.is_win(
self.opponent[self.color]) < 1:
self.expansion(node)
#Recursive Expansion
children = node.child_nodes
for child in children:
if not child.expanded:
#print("ROLLOUT RECURSE")
self.rollout(child, steps + 1)
#print("END ROLLOUT RECURSE")
return
else:
pass
def backpropogate(self, node: MonteCarloTree):
#print("BACKPROPOGATE")
#print(node)
#update UCB1 value too
if node.parent_node is None:
#print("reached root node")
return
if node.parent_node is not None:
node.parent_node.ucb1_eval += node.ucb1_eval
node.parent_node.no_of_wins += node.no_of_wins
node.parent_node.no_of_steps += node.no_of_steps
node.board.undo()
#print("Recurse-BACKPROPOGATE")
self.backpropogate(node.parent_node)
#print("End Recurse Backpropogate")
def monte_carlo_tree_search(self, node: MonteCarloTree):
#print("MCTS SEARCH START")
self.expansion(node)
#print(node)
for nd in node.child_nodes: #should be a while loop, and always start from the root
self.rollout(nd)
root_wins = node.no_of_wins
self.ucb1_evaluation(root_wins, nd)
best_child = self.choose_best_child(node)
return best_child.move
def choose_best_child(self, node: MonteCarloTree):
children = sorted(node.child_nodes,
key=attrgetter('ucb1_eval'),
reverse=True)
return children[0]
def ucb1_evaluation(self, no_of_wins_r, node: MonteCarloTree):
#print("No. of steps = " + str(node.no_of_steps))
#print("No. of wins at root = " + str(no_of_wins_r))
#print("No. of wins at this node = " + str(node.no_of_wins))
node.ucb1_eval = node.no_of_steps + 2 * math.sqrt(
(math.log(no_of_wins_r)) / (node.no_of_wins + 1))
return
'''
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
def get_move(self, move):
# make opponents move
if len(move) != 0:
self.board.make_move(move, self.opponent[self.color])
# switch turn
else:
self.color = 1
# find player 1 next move
moves = self.board.get_all_possible_moves(self.color)
alpha = -INFINITY
beta = INFINITY
result = self.move_ordering(self.color)
for move in moves:
for m in move:
limit = 0
self.board.make_move(m, self.color)
val = self.min_value(limit + 1, alpha, beta)
if val > alpha:
alpha = val
result = m
self.board.undo()
self.board.make_move(result, self.color)
return result
def max_value(self, limit, alpha, beta):
if limit == MINIMAX_DEPTH or self.board.is_win(
self.color) == self.color:
return self.heuristic()
#alpha = -INFINITY # result = -infinity
# move = self.move_ordering(self.color)
# if not move:
# return alpha
# self.board.make_move(move,self.color)
# v = self.min_value(limit+1,alpha,beta)
# self.board.undo()
# if v >= beta:
# return INFINITY
# alpha = max(alpha,v)
val = -INFINITY
moves = self.board.get_all_possible_moves(self.color)
for move in moves:
for m in move:
self.board.make_move(m, self.color)
val = max(val, self.min_value(limit + 1, alpha, beta))
self.board.undo()
alpha = max(alpha, val)
if alpha >= beta:
return val
return val
def min_value(self, limit, alpha, beta):
if limit == MINIMAX_DEPTH or self.board.is_win(
self.color) == self.opponent[self.color]:
return self.heuristic()
#beta = INFINITY # result = infinity
# move = self.move_ordering(self.opponent[self.color])
# if not move:
# return beta
# self.board.make_move(move,self.opponent[self.color])
# v = self.max_value(limit+1,alpha,beta)
# self.board.undo()
# if alpha >= v:
# return -INFINITY
# beta = min(beta,v)
# return beta
val = INFINITY
moves = self.board.get_all_possible_moves(self.opponent[self.color])
for move in moves:
for m in move:
self.board.make_move(m, self.opponent[self.color])
val = min(val, self.max_value(limit + 1, alpha, beta))
self.board.undo()
beta = min(beta, val)
if alpha >= beta: # pruning - go to next move (?)
return val
return val
def heuristic(self):
black = 0
white = 0
#if (self.board.black_count+self.board.white_count) > (0.4* self.board.p * self.board.col): # Decide between early game and mid,end game
for row in range(self.board.row):
for col in range(self.board.col):
if self.board.board[row][col].color == "B":
if self.board.board[row][col].is_king: # King = 10
black += self.board.row
else:
black += (5 + self.board.board[row][col].row -
(0.5 * self.board.row)
) # reg piece = 5 + rows on oponent side
elif self.board.board[row][col].color == "W":
if self.board.board[row][col].is_king: # King = 10
white += self.board.row
else:
white += (5 + (self.board.row * 0.5) -
self.board.board[row][col].row
) # reg piece = 5 + rows on oponent s
if self.color == 1:
return black - white
else:
return white - black
def move_ordering(self, player):
best_move_value = -INFINITY if (player == self.color) else INFINITY
moves = self.board.get_all_possible_moves(player)
if moves:
best_move = moves[0][0]
else:
return False
for move in moves:
for m in move:
self.board.make_move(m, player)
v = self.heuristic()
if v > best_move_value if (
player == self.color) else v < best_move_value:
best_move_value = v
best_move = m
self.board.undo()
return best_move
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
def get_move(self, move):
if len(move) != 0:
self.board.make_move(move, self.opponent[
self.color]) # Run opponent's move for self.board
else:
self.color = 1
root = Tree(self.opponent[self.color]) #Tree root
self.rec_tree(root, search_depth) #Set up tree
self.rec_min_max_heuristic(root)
avail_moves = root.value[list(root.value)[0]]
#cur_move = avail_moves[0]
cur_move = avail_moves[randint(0, len(avail_moves) - 1)]
#print(avail_moves)
self.board.make_move(cur_move, self.color) # Make the optimal move
move = cur_move
return move
def ftu(self, color): #Function to use (min vs max by color)
if color == self.color: # Calculate Min
return max
else: # Calculate Max
return min
def min_max(self, children,
color): # Returns dict -> {Max/min value: Moves to get here}
ftu = self.ftu(color) #Use corresponding min or max depending on color
value_map = {}
for child in children:
for v in child.value.keys():
value_map.setdefault(v, []).append(
child.move
) # D: {heuristic value: Move to make to get here}
# print(value_map)
return {ftu(value_map): value_map[ftu(value_map)]}
def board_points(
self): # 5 + row number for pawns, 5 + row number + 2 for kings
'''
def board_points(self): # 5 + row number for pawns, 5 + row number + 2 for kings
king_pts_value = 5 + (
self.row - 1) + 2 # 5 pts for piece, self.row -1 pts for pts at end of board, + 1 for being king
pts = 0
for i in range(self.row):
for j in range(self.col):
checker = self.board.board[i][j]
if checker.color == 'B': # For black side pieces
if checker.is_king:
pts += king_pts_value
else:
pts += 5 + checker.row
elif checker.color == 'W': # FOr white side pieces
# pts -= (11 - checker.row) # 5 + (6 - Row)
if checker.is_king:
pts -= king_pts_value
else:
pts -= (5 + (
self.row - checker.row - 1)) # 5 + (Num of rows - Row - 1) eg. 5x5 board, 5th row is 5(num) - 4(row) -1 = 0
if abs(pts) > 2:
self.dif_val = True
# if debug: print(color(root.color), pts, -pts)
return pts if self.color == 1 else -pts # BLACK(1) GOES FIRST, so positive points, if self.color == white(2), then return white pieces as positive points
'''
pts = 0
for i in range(self.row):
for j in range(self.col):
checker = self.board.board[i][j]
if checker.color == 'B': # For black side pieces
pts += 5 + checker.row
if checker.is_king: # 2 additional pts for king
pts += 2
elif checker.color == 'W': # FOr white side pieces
pts -= 11 - checker.row # 5 + (6 - Row)
if checker.is_king: # 2 additional pts for king
pts -= 2
return pts if self.color == 1 else -pts
def print_tree(self, root, level=0):
# print("PRINTING TREE")
print("\t" * level, root.value, "->", root.move)
if len(root.children) != 0: # Not Leaf node
for child in root.children:
self.print_tree(child, level + 1)
def rec_tree(self, root: Tree, level=1): #Create tree up to depth level
if level == 0:
pass
else:
if root.move is not None: # Not root of tree
self.board.make_move(root.move, root.color)
#Check if win here maybe?
avail_moves = self.board.get_all_possible_moves(
self.opponent[root.color])
for i in range(len(avail_moves)):
for j in range(len(avail_moves[i])):
#print(root)
root.children.append(
Tree(self.opponent[root.color], avail_moves[i][j]))
for child in root.children:
self.rec_tree(child, level - 1)
if root.move is not None:
self.board.undo()
def rec_min_max_heuristic(self,
root: Tree): #Apply min_max heuristic to tree
if root.move is not None: #If not root of tree, make the move required to get here
self.board.make_move(root.move, root.color)
if len(root.children) == 0: #Passed node has no children
pass #Evaluate heuristic for board(and return?)
root.value = {self.board_points(): []}
else: #Evaluate rec_heuristic for children, then retrieve values and apply min/max as appropriate
for child in root.children:
self.rec_min_max_heuristic(child)
root.value = self.min_max(root.children, root.color)
if root.move is not None:
self.board.undo()
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
self.f = open("debug.txt", "w")
def heuristic_black(self):
count_b = 0
count_w = 0
for i in range(int(len(self.board.board) / 2)):
for j in range(len(self.board.board[i])):
if self.board.board[i][j].color == 1:
if self.board.board[i][j].is_king == True:
count_b += 10
else:
count_b += 5
else:
if self.board.board[i][j].is_king == True:
count_w += 10
else:
count_w += 7
for i in range(int(len(self.board.board) / 2), len(self.board.board)):
for j in range(len(self.board.board[i])):
if self.board.board[i][j].color == 1:
if self.board.board[i][j].is_king == True:
count_b += 10
else:
count_b += 7
else:
if self.board.board[i][j].is_king == True:
count_w += 10
else:
count_w += 5
# for i in self.board.board:
# for j in i:
#
# if j.color == 1:
# if j.is_king == True:
# count_b += 7 + self.row
# else:
# count_b += 5 + (self.row - j.row)
# elif j.color == 2:
# if j.is_king == True:
# count_w += 7 + self.row
# else:
# count_w += 5 + j.row
return count_b - count_w
def get_move(self, move):
if len(move) != 0:
self.board.make_move(move, self.opponent[self.color])
else:
self.color = 1
moves = self.board.get_all_possible_moves(self.color)
self.f.write("Curr Moves: " + str(moves) + '\n')
if len(moves) == 1 and len(moves[0]) == 1:
move = moves[0][0]
self.board.make_move(move, self.color)
return move
move = self.minimax(moves)
self.f.write("Chosen Move: " + str(move) + '\n')
# index = randint(0,len(moves)-1)
# inner_index = randint(0,len(moves[index])-1)
# move = moves[index][inner_index]
self.board.make_move(move, self.color)
return move
def minimax(self, moves):
dic_l1 = dict()
for peice in range(len(moves)):
for i in range(len(moves[peice])):
move = moves[peice][i]
self.board.make_move(move, self.color)
if self.board.is_win(self.color) == self.color:
self.board.undo()
return moves[peice][i]
l2_moves = self.board.get_all_possible_moves(
self.opponent[self.color])
# print("Opponent Moves: \n peice: ",peice, "\n dir: ",i, "\nMoves\n", l2_moves)
dic_l2 = dict()
for opp_peice in range(len(l2_moves)):
for j in range(len(l2_moves[opp_peice])):
move = l2_moves[opp_peice][j]
self.board.make_move(move, self.opponent[self.color])
l3_moves = self.board.get_all_possible_moves(
self.color)
dic_l3 = dict()
# print("L3 ",l3_moves)
for my_peice in range(len(l3_moves)):
flag = 0
for k in range(len(l3_moves[my_peice])):
move = l3_moves[my_peice][k]
self.board.make_move(move, self.color)
value = -1
if self.color == 1:
value = (self.board.black_count /
(self.board.black_count +
self.board.white_count)) * 100
else:
value = (self.board.white_count /
(self.board.black_count +
self.board.white_count)) * 100
key = str(my_peice) + ' ' + str(k)
# print(key, ' ', value)
dic_l3[key] = value
self.board.undo()
if self.board.is_win(self.color) == self.color:
flag = 1
break
if flag == 1:
break
if len(dic_l3) == 0:
key = str(opp_peice) + ' ' + str(j)
dic_l2[key] = int(0x40000)
self.board.undo()
else:
inverse = [(value, key)
for key, value in dic_l3.items()]
l2_value = max(inverse)[0]
key = str(opp_peice) + ' ' + str(j)
dic_l2[key] = l2_value
self.board.undo()
if len(dic_l2) == 0:
key = str(peice) + ' ' + str(i)
dic_l1[key] = int(-0x40000)
self.board.undo()
else:
inverse = [(value, key) for key, value in dic_l2.items()]
l1_value = min(inverse)[0]
key = str(peice) + ' ' + str(i)
dic_l1[key] = l1_value
self.board.undo()
inverse = [(value, key) for key, value in dic_l1.items()]
l0_value = max(inverse)[1]
# print(dic_l1)
# print(l0_value)
x, y = l0_value.split(' ')
return moves[int(x)][int(y)]
class StudentAI():
def __init__(self,col,row,p):
self.col = col
self.row = row
self.p = p
self.board = Board(col,row,p)
self.board.initialize_game()
self.color = ''
self.opponent = {1:2,2:1}
self.color = 2
# new params
'''
new data clearification:
feature_matrix = [[X1, X2, ..., X_feature_size, Y],
...
[X1m, X2m, ..., X_feature_size_m, Y]]
'''
self.movecount = 0
self.feature_size = 5
self.thetas = np.random.rand(self.feature_size)
self.feature_matrix = np.empty((0, self.feature_size))
def get_move(self, move):
#print(self.color)
if len(move) != 0:
self.board.make_move(move,self.opponent[self.color])
else:
self.color = 1
moves = self.board.get_all_possible_moves(self.color)
def make_action(self,state):
# TODO: implement this to return action with max Q value
for a in get_all_action(state):
return action with max(self.Q_table[state, action])
#self.train()
self.simulate_lr(self.color)
#index = randint(0,len(moves)-1)
#inner_index = randint(0,len(moves[index])-1)
#move = moves[index][inner_index]
#print(moves)
move = self.minimax_move(moves)
#move = self.monte_carlo_tree([m for chess in moves for m in chess], 10, 10)
self.board.make_move(move, self.color)
self.movecount += 1
return move
def minimax_move(self, moves: [list]) :
best = []
max_value = - math.inf
depth = 4
for chess in moves:
for move in chess:
val = self.max_value(move, depth, -math.inf, math.inf)
if val > max_value:
best = [move]
max_value = val
elif val == max_value:
best.append(move)
if len(best) == 1:
return best[0]
# print(len(best))
# best[randint(0,len(best)-1)]
return self.monte_carlo_tree(best, 10, 10)
def explore(self, state):
epsilon = 0.2
if random.uniform(0,1) < epsilon:
# TODO : implement select random move
else:
# TODO: implement explore max reward
def monte_carlo_tree(self, moves: [], simulate_times: int, s_parent: int):
best_uct = 0
best_move = 0
for move in moves:
wins = self.simulate(move, simulate_times)
uct = wins/simulate_times + math.sqrt(2*math.log(s_parent)/simulate_times)
if uct > best_uct:
best_move = move
return best_move
def simulate(self, move, s):
wins = 0
board = copy.deepcopy(self.board)
board.make_move(move, self.color)
for i in range(s):
curr_turn = self.opponent[self.color]
t = 0
for turn in range(20):
if board.is_win(self.color) == self.color:
wins = 1
self.undo(board,t)
break
elif board.is_win(self.opponent[self.color]) == self.opponent[self.color]:
self.undo(board, t)
break
moves = board.get_all_possible_moves(curr_turn)
index = randint(0,len(moves)-1)
inner_index = randint(0,len(moves[index])-1)
board.make_move(moves[index][inner_index], curr_turn)
curr_turn = self.opponent[curr_turn]
t += 1
else:
wins = 0.5
self.undo(board, t)
return wins
def undo(self, board, times):
for _ in range(times):
board.undo()
def train_one_episode(self):
# This is training function for one episode, start a new board and
# update Q value until win or loss
# QUESTION IS HOW TO DECIDE OPPONENT MOVE? BY SELF-TRAIN? AND HOW TO SELF-TRAIN?
new_board = Board()
new_board.initialize_game()
turn = ''
def min_value(self, move, depth, alpha, beta):
self.board.make_move(move, self.opponent[self.color])
if depth == 0:
#u = self.utility(self.board)
u = self.utility_with_theta(self.board)
self.board.undo()
return u
min_val = math.inf
for chess in self.board.get_all_possible_moves(self.color):
for move in chess:
min_val = min(self.max_value(move, depth - 1, alpha, beta), min_val)
beta = min(beta, min_val)
if alpha >= beta:
self.board.undo()
return min_val
self.board.undo()
return min_val
def max_value(self, move, depth, alpha, beta):
self.board.make_move(move, self.color)
if depth == 0:
#u = self.utility(self.board)
u = self.utility_with_theta(self.board)
self.board.undo()
return u
max_val = - math.inf
for chess in self.board.get_all_possible_moves(self.opponent[self.color]):
for move in chess:
max_val = max(self.min_value(move, depth - 1, alpha, beta), max_val)
alpha = max(alpha, max_val)
if alpha >= beta:
self.board.undo()
return max_val
self.board.undo()
return max_val
def utility(self, board):
bking, wking = 0, 0
for r in range(self.board.row):
for c in range(self.board.col):
if self.board.board[r][c].color == "B":
bking += self.board.board[r][c].is_king
elif self.board.board[r][c].color == "W":
wking += self.board.board[r][c].is_king
bback = sum(self.board.board[0][i].color == "B" for i in range(self.board.col))
wback = sum(self.board.board[self.board.row-1][i].color == "W" for i in range(self.board.col))
bedge = sum(self.board.board[i][0].color == "B"+self.board.board[i][self.board.col-1].color == "B" for i in range(self.board.row))
wedge = sum(self.board.board[i][0].color == "W"+self.board.board[i][self.board.col-1].color == "W" for i in range(self.board.row))
time_param = math.log(self.movecount)
b = 3*self.board.black_count + bking * time_param + bback * (1/time_param) + bedge * (1/time_param)
w = 3*self.board.white_count + wking * time_param + wback * (1/time_param) + wedge * (1/time_param)
# b = self.board.black_count
# w = self.board.white_count
#print("black:",b," white:",w)
return b-w if self.color == 1 else w-b
####################################################
### Training heuristics using Linear Regression ####
####################################################
def utility_with_theta(self, board):
X_black, X_white = self.get_X(board)
b = X_black.dot(self.thetas)
w = X_white.dot(self.thetas)
return b - w if self.color == 1 else w - b
def get_X(self, board):
bking, wking = 0, 0
for r in range(self.board.row):
for c in range(self.board.col):
if self.board.board[r][c].color == "B":
bking += self.board.board[r][c].is_king
elif self.board.board[r][c].color == "W":
wking += self.board.board[r][c].is_king
bback = sum(self.board.board[0][i].color == "B" for i in range(self.board.col))
wback = sum(self.board.board[self.board.row-1][i].color == "W" for i in range(self.board.col))
bedge = sum(self.board.board[i][0].color == "B"+self.board.board[i][self.board.col-1].color == "B" for i in range(self.board.row))
wedge = sum(self.board.board[i][0].color == "W"+self.board.board[i][self.board.col-1].color == "W" for i in range(self.board.row))
X_black = np.array([self.board.black_count, bking, bback, bedge, self.movecount])
X_white = np.array([self.board.white_count, wking, wback, wedge, self.movecount])
return X_black, X_white
# def model(self, X, thetas):
# # X = [count, king, back, edge, time]
# # thetas = [t1, t2, t3, t4, t5]
# #return thetas[0] * X[0] + thetas[1] * X[1] + thetas[2] * X[2] + thetas[3] * X[3] + thetas[4] * X[4]
# return X*thetas
def train(self):
# TODO: Training thetas by linear regression and mean square error gradient descent
self.thetas = np.random.rand(5)
epoch = 0.01
alpha = 0.05
while True:
thetas = thetas - self.Gradient() * alpha
if self.Gradient() * alpha < epoch:
break
return thetas
def simulate_times(self, color, simulate_times):
wins = 0
for _ in simulate_times:
wins += self.simulate_lr(color)
return wins
def simulate_lr(self, color):
# simulate one time
# record all X features to feature_matrix
# update the y value accordingly
print("entering simulations")
newboard = Board(self.col, self.row, self.p)
newboard.initialize_game()
feature_list_b = []
feature_list_w = []
win = 0
### TODO: Fixing Current move in a new board
curr_turn = self.opponent[color]
for turn in range(50):
if newboard.is_win(color) == color:
win = 1
break
elif newboard.is_win(self.opponent[color]) == self.opponent[color]:
break
move = self.minimax_move(newboard.get_all_possible_moves(curr_turn))
newboard.make_move(move, curr_turn)
b, w = self.get_X(self.board)
feature_list_b.append(b)
feature_list_w.append(w)
self.feature_matrix = np.append(self.feature_matrix, np.array([b, w]), axis=0)
print(self.feature_matrix)
curr_turn = self.opponent[curr_turn]
else:
win = 0.5
# matrix = np.array([feature_list_b, feature_list_w])
# feature_matrix = np.hstack((matrix, np.zeros((matrix.shape[0], 1))))
# TODO: Fixing y value update
if win == 1 and color == 1:
for fb in feature_list_b:
index = np.where(fb in self.feature_matrix[:, 0:self.feature_size])
if index == []:
self.feature_matrix = np.append(self.feature_matrix, np.array([b, w]), axis=0)
self.feature_matrix[index, self.feature_size] += 1
elif win == 0 and color == 1:
for fw in feature_list_w:
index = np.where(fw in self.feature_matrix[:, 0:self.feature_size])
if index == []:
self.feature_matrix = np.append(self.feature_matrix, np.array([b, w]), axis=0)
self.feature_matrix[index, self.feature_size] += 1
return win
def MSE(self, thetas):
# TODO: get mean square error by simulation
wins = self.simulate_times(self.color, 20)
utility = self.model(self.get_X(self.board), thetas)
def Gradient(self, mse):
# TODO: calculate gradient according to mse
pass
def move_by_qtable(self):
# TODO: maybe write a function to choose move from feature matrix accordingly
pass
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
def get_move(self, move):
if len(move) != 0:
self.board.make_move(move, self.opponent[self.color])
else:
self.color = 1
new_move = self.MiniMax()
if len(new_move.seq) == 0:
random_moves = self.board.get_all_possible_moves(self.color)
index = randint(0, len(random_moves) - 1)
inner_index = randint(0, len(random_moves[index]) - 1)
random_move = random_moves[index][inner_index]
self.board.make_move(random_move, self.color)
return random_move
else:
self.board.make_move(new_move, self.color)
return new_move
def get_value(self, input_color):
# add more condition
value = 0
count = 0
if input_color == 1:
color = 'B'
for row in range(0, self.row):
for col in range(0, self.col):
checker = self.board.board[row][col]
if checker.color == color:
# more weight to cornor
if (checker.col == 0 and checker.row == self.row - 1):
value += 3
if (checker.col == self.col - 1
and checker.row == self.row - 1):
value += 3
if checker.is_king:
# not addding row num
# so that moving forward and backward is equal
value += 10
else:
value += checker.row
# give more weight
# encourage pawns to become king
if checker.row + 1 > self.row / 2:
value += (checker.row + 1 - self.row / 2) * 2
if row == self.row - 1:
value += 5
elif col == self.col - 1 or row * col == 0:
value += 2
elif checker.color != color and checker.color != '.':
if checker.is_king:
value -= 2 * (checker.row)
# value -= 10
else:
value -= 0.5 * (checker.row)
if row == self.row - 1 or col == self.col - 1 or row * col == 0:
value -= 2
else:
color = 'W'
for row in range(0, self.row):
for col in range(0, self.col):
checker = self.board.board[row][col]
if checker.color == color:
# more weight to corner
if (checker.col == 0 and checker.row == 0):
value += 3
if (checker.col == self.col - 1 and checker.row == 0):
value += 3
if checker.is_king:
value += 10
else:
abs_row = 8 - checker.row
value += abs_row
if abs_row > self.row / 2:
value += (abs_row - self.row / 2) * 2
if row == 0:
value += 5
elif col == self.col - 1 or row * col == 0:
value += 2
# handle Tie
elif checker.color != color and checker.color != '.':
if checker.is_king:
value -= 2 * (checker.row)
# value -= 10
else:
value -= 0.5 * (checker.row)
if row == self.row - 1 or col == self.col - 1 or row * col == 0:
value -= 2
return value
def Cutoff(self, depth):
return depth >= 4
# B -> Max
def get_MaxVal(self, depth, alpha, beta):
color = self.color
if self.Cutoff(depth):
return self.get_value(self.color)
val = -inf
next_moves = self.board.get_all_possible_moves(color)
for checker_index in range(0, len(next_moves)):
for move_index in range(0, len(next_moves[checker_index])):
next_move = next_moves[checker_index][move_index]
self.board.make_move(next_move, color)
val = max(val, self.get_MinVal(depth + 1, alpha, beta))
if val >= beta:
self.board.undo()
return val
alpha = max(alpha, val)
self.board.undo()
return val
def get_MinVal(self, depth, alpha, beta):
color = self.opponent[self.color]
if self.Cutoff(depth):
return self.get_value(self.color)
val = inf
next_moves = self.board.get_all_possible_moves(color)
for checker_index in range(0, len(next_moves)):
for move_index in range(0, len(next_moves[checker_index])):
next_move = next_moves[checker_index][move_index]
self.board.make_move(next_move, color)
val = min(val, self.get_MaxVal(depth + 1, alpha, beta))
if val <= alpha:
self.board.undo()
return val
beta = min(beta, val)
self.board.undo()
return val
def MiniMax(self):
alpha = -inf
beta = inf
best_move = Move([])
moves = self.board.get_all_possible_moves(self.color)
for checker_index in range(0, len(moves)):
for move_index in range(0, len(moves[checker_index])):
move = moves[checker_index][move_index]
if len(move) == 0:
pass
depth = 0
# *(1) start: undo later
self.board.make_move(move, self.color)
value = self.get_MinVal(depth + 1, alpha, beta)
if value > alpha:
alpha = value
best_move = move
# *(1) end
self.board.undo()
return best_move
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
self.ct = 0
#self.dif_val = False
self.size = self.col * self.row
if self.size < 40: #6x6
#print(8)
self.search_depth = 8
elif self.size < 50: #7x7
#print(7)
self.search_depth = 5
elif self.size < 80: #8x8
#print(6)
self.search_depth = 4
else:
self.search_depth = 4
def get_move(self, move):
if len(move) != 0:
self.board.make_move(move, self.opponent[
self.color]) # Run opponent's move for self.board
else:
self.color = 1
try:
self.search_depth
except NameError:
print("ERROR")
search_depth = 5
if self.size < 40: #6x6
if self.ct == 5:
self.search_depth += 1 #9
elif self.ct == 10:
self.search_depth += 1 #10
elif self.size < 50: #7x7
if self.ct == 2:
self.search_depth += 1 #6
elif self.ct == 5:
self.search_depth += 1 #7
if self.ct == 10:
self.search_depth += 1 #8
elif self.ct == 15:
self.search_depth += 1 #9
elif self.ct == 20:
self.search_depth += 1 #10
elif self.size < 80: #8x8
if self.ct == 3:
self.search_depth += 1 #5
elif self.ct == 5:
self.search_depth += 1 #6
elif self.ct == 7:
self.search_depth += 1 #7
elif self.ct == 11:
self.search_depth += 1 #8
else:
if self.ct == 10:
self.search_depth += 1
elif self.ct == 20:
self.search_depth += 2
root = Tree(self.opponent[self.color]) # Tree root
#print('Detph', self.search_depth, self.ct)
self.rec_tree(root, self.search_depth) # Set up tree
self.rec_min_max_heuristic(root)
#self.rec_abp_heuristic(root)
#self.rec_abp_v2(root)
avail_moves = root.value[list(root.value)[0]]
#cur_move = avail_moves[randint(0,len(avail_moves)-1)]
cur_move = avail_moves[0]
'''
print("ALL MOVES")
moves = self.board.get_all_possible_moves(self.color)
for i, checker_moves in enumerate(moves):
print(i, ':[', end="")
for j, move in enumerate(checker_moves):
print(j, ":", move, end=", ")
print("]")
print("AVAIL MOVES")
#print(avail_moves)
for i, checker_moves in enumerate(avail_moves):
print(i, ':[', end="")
for j, move in enumerate(checker_moves):
print(j, ":", move, end=", ")
print("]")
'''
#if self.dif_val:
if debug: print("##########TREE##########")
self.print_tree(root)
if debug: print("##########TREE##########")
# self.dif_val = False
self.board.make_move(cur_move, self.color) # Make the optimal move
move = cur_move
return move
# Board Heuristic
def board_points(
self): # 5 + row number for pawns, 5 + row number + 2 for kings
king_pts_value = 5 + (
self.row - 1
) + 5 #5 pts for piece, self.row -1 pts for pts at end of board, + 1 for being king
pts = 0
b_pawns = set()
b_kings = set()
w_pawns = set()
w_kings = set()
for i in range(self.row):
for j in range(self.col):
checker = self.board.board[i][j]
if checker.color == "B": #Black
if checker.is_king:
b_kings.add((i, j))
else:
b_pawns.add((i, j))
elif checker.color == "W": #White
if checker.is_king:
w_kings.add((i, j))
else:
w_pawns.add((i, j))
# if b_pawns == set():
# print("-" * 20)
# self.board.show_board()
# b_pawns = set()
# b_kings = set()
# w_pawns = set()
# w_kings = set()
# for i in range(self.row):
# for j in range(self.col):
# checker = self.board.board[i][j]
# if checker.color == "B": #Black
# if checker.is_king:
# b_kings.add((i,j))
# else:
# b_pawns.add((i,j))
# elif checker.color == "W": #White
# if checker.is_king:
# w_kings.add((i,j))
# else:
# w_pawns.add((i,j))
for pawn in b_pawns:
pts += 5 + pawn[0]
for pawn in w_pawns:
pts -= (5 + (self.row - pawn[0] - 1))
for king in b_kings:
pts += king_pts_value
dist = 0
for w in w_kings:
dist += sqrt((king[0] - w[0])**2 + (king[1] - w[1])**2)
for w in w_pawns:
dist += sqrt((king[0] - w[0])**2 + (king[1] - w[1])**2)
if len(w_kings) + len(w_pawns) != 0:
pts -= dist / (len(w_kings) + len(w_pawns))
for king in w_kings:
pts -= king_pts_value
dist = 0
for b in b_kings:
dist += sqrt((king[0] - b[0])**2 + (king[1] - b[1])**2)
for b in b_pawns:
dist += sqrt((king[0] - b[0])**2 + (king[1] - b[1])**2)
if len(b_kings) + len(b_pawns) != 0:
pts += dist / (len(b_kings) + len(b_pawns))
#if abs(pts) > 2:
# self.dif_val = True
#if debug: print(color(root.color), pts, -pts)
return pts if self.color == 2 else -pts #BLACK(1) GOES FIRST, so positive points, if self.color == white(2), then return white pieces as positive points
def print_tree(self, root, level=0):
if not debug:
return
print("\t" * level, color(root.color), root.value, "->", root.move)
if len(root.children) != 0: # Not Leaf node
for child in root.children:
self.print_tree(child, level + 1)
def rec_tree(self, root: Tree, level=1): # Create tree up to depth level
if level == 0:
pass
else:
if root.move is not None: # Not root of tree
self.board.make_move(root.move, root.color)
# Check if win here maybe?
avail_moves = self.board.get_all_possible_moves(
self.opponent[root.color])
for i in range(len(avail_moves)):
for j in range(len(avail_moves[i])):
# print(root)
root.children.append(
Tree(self.opponent[root.color], avail_moves[i][j]))
for child in root.children:
self.rec_tree(child, level - 1)
if root.move is not None:
self.board.undo()
# MinMax Functions
def ftu(self, color): # Function to use (min vs max by color)
if color == self.color: # Calculate Max
return max
else: # Calculate Min
return min
def min_max(self, children,
color): # Returns dict -> {Max/min value: Moves to get here}
ftu = self.ftu(
color) # Use corresponding min or max depending on color
value_map = {}
for child in children:
for v in child.value.keys():
value_map.setdefault(v, []).append(
child.move
) # D: {heuristic value: Move to make to get here}
# print(value_map)
return {ftu(value_map): value_map[ftu(value_map)]}
def rec_min_max_heuristic(self,
root: Tree): # Apply min_max heuristic to tree
if root.move is not None: # AKA this is root, the move is what opponent made to get here (none so we don't have to redo move on our board)
self.board.make_move(root.move, root.color)
if len(root.children) == 0: # Passed node has no children
# Evaluate heuristic for board(and return?)
root.value = {
self.board_points(): []
} # Value will be dict with key = heuristic points and value = all the moves that result in that many points
else: # Evaluate rec_heuristic for children, then retrieve values and apply min/max as appropriate
for child in root.children:
self.rec_min_max_heuristic(child)
root.value = self.min_max(root.children, root.color)
if root.move is not None:
self.board.undo(
) # Undo move to revert action (done for searching) and return to parent
# AlphaBeta Functions
def set_alpha_beta(self, root, child, color):
ftu = self.ftu(color)
if child.value is None:
print(child)
if root.value is None:
root.value = {}
if color == self.color: # Max aka update alpha (This ai's turn)
# return ftu(alpha, ftu(child.value)), beta
if root.alpha < ftu(child.value):
root.alpha = ftu(child.value)
root.value.setdefault(root.alpha, []).append(child.move)
else: # Min aka update beta (Opponent's turn)
# return alpha, ftu(beta, ftu(child.value))
if root.beta > ftu(child.value):
root.beta = ftu(child.value)
root.value.setdefault(root.beta, []).append(child.move)
def rec_abp_heuristic(self,
root: Tree,
alpha=-999,
beta=999,
level=0): # Alpha Beta Pruning
if debug:
print("\t" * level, color(root.color), "Enter: ", root.value, "->",
root.move)
old_val = root.value
if root.move is not None: # AKA this is root, the move is what opponent made to get here (none so we don't have to redo move on our board)
self.board.make_move(root.move, root.color)
#self.board.show_board()
if len(
root.children
) == 0: # Passed node has no children aka this is lowest level/leaf
root.value = {self.board_points(): []}
if debug:
print("\t" * level, "LEAF: ", root.value, "->", root.move)
else: # Evaluate heuristic for child, retrieve value, update alphabeta, continue with next child if appropriate
root.alpha = alpha
root.beta = beta
if debug: print("\t" * 16, "CHILDREN:", end=" ")
for child in root.children:
if debug: print(child.move, end=", ")
if debug: print("(", color(self.opponent[root.color]), ")", sep="")
for child in root.children:
if root.alpha >= root.beta: # Break out of loop once alpha >= beta (Pruning)
if debug: print("PRUNING")
break
self.rec_abp_heuristic(child, root.alpha, root.beta, level + 1)
self.set_alpha_beta(
root, child, root.color
) # Apply alpha/beta values based on min/max of child to current node
if debug:
print("\t" * level, color(root.color), "New Value: ",
root.value, "->", root.move)
if root.move is not None:
self.board.undo()
if debug:
print("\t" * level, color(root.color), "Exit: ", root.value, "->",
root.move)
#print(max(list(root.value), key = abs), "\t", root.move, "->", root.value)
#if abs(max(list(root.value), key = abs)) > 2:
#print("\t" * level, "Enter: ", old_val, "->", root.move)
#print("\t" * level, "Exit: ", root.value, "->", root.move)
def rec_abp_v2(self, root: Tree, alpha=-999, beta=999):
if root.move is not None: # AKA this is root, the move is what opponent made to get here (none so we don't have to redo move on our board)
self.board.make_move(root.move, root.color)
else:
root.value = {}
if len(root.children) == 0:
root.value = self.board_points()
if root.move is not None:
self.board.undo()
return root.value
else:
if color == self.color: #MaximizingPlayer
#val = -999
for child in root.children:
'''
val = max(val, rec_abp_v2(child, alpha, beta))
alpha = max(alpha, val)
'''
val = self.rec_abp_v2(child, alpha, beta)
if alpha > val: #Alpha > Val
root.alpha = alpha
else: #Val > Alpha
alpha = val
if root.move is None: #Root node, ie save the move to get here
root.value.setdefault(alpha, []).append(child.move)
root.alpha = alpha
if alpha >= beta:
break
if root.move is not None:
self.board.undo()
return alpha
else: #Minimizing Player
#val = 999
for child in root.children:
'''
val = min(val, alphabeta(child, alpha, beta))
beta = min(val, beta)
'''
val = self.rec_abp_v2(child, alpha, beta)
if beta < val: #Beta < Val
root.beta = beta
else:
beta = val
if root.move is None:
root.value.setdefault(beta, []).append(child.move)
root.beta = beta
if alpha >= beta:
break
if root.move is not None:
self.board.undo()
return beta
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
self.depth = 4
self.a = -math.inf
self.b = math.inf
def get_move(self, move):
if len(move) != 0:
self.board.make_move(move, self.opponent[self.color])
else:
self.color = 1
moves = self.board.get_all_possible_moves(self.color)
# index = randint(0, len(moves) - 1)
# inner_index = randint(0, len(moves[index]) - 1)
# move = moves[index][inner_index]
move = self.select_move(moves)
# print(move)
self.board.make_move(move, self.color)
return move
def select_move(self, moves):
"""take a list of moves and select the best one,
return move"""
copySelf = copy.deepcopy(self)
return abPruning(copySelf, 0, self.a, self.b, self.color)
# return simple_search(copySelf)
def heuristic_func(self, move, color):
"""take a move, evaluate it's heuristic value
return the value"""
result = 0
colorDict = {"B": 1, "W": 2, ".": 0}
self.board.make_move(move, color)
# print (self.board.board)
black = []
white = []
for row in self.board.board:
for checker in row:
if colorDict[checker.get_color()] == 1:
black.append(checker.get_location())
else:
white.append(checker.get_location())
# for row in self.board.board:
# for checker in row:
# add = 1 if colorDict[checker.get_color()] == self.color else -1
# y = checker.get_location()[1]
# if self.color == 1:
# # if getProtected(checker.get_location(), black) and add == 1:
# # result += 5
# if checker.is_king:
# result += (self.board.row + 1 + y) * add
# else:
# result += (y + 5) * add
# else:
# # if getProtected(checker.get_location(), white) and add == 1:
# # result += 5
# if checker.is_king:
# result += (self.board.row + 1) * add
# else:
# result += (4 + self.board.row - y) * add
for row in self.board.board:
for checker in row:
y = checker.get_location()[1]
x = checker.get_location()[0]
if colorDict[checker.get_color()] == self.color:
if self.color == 2 and y == self.board.row - 1:
result += 0.5
elif self.color == 1 and y == 0:
result += 0.5
if x == 0 or x == self.board.col - 1:
result += 0.25
if checker.is_king:
result += 3.0 + 0.5 * abs(y - (self.board.row / 2.0))
else:
result += 1.5
else:
if checker.is_king:
result -= 3.0
else:
result -= 1.5
self.board.undo()
return result
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
def get_move(self, move):
if len(move) != 0:
self.board.make_move(move, self.opponent[
self.color]) # Run opponent's move for self.board
else:
self.color = 1
root = Tree(self.opponent[self.color]) #Tree root
self.rec_tree(root, search_depth)
self.rec_heuristic(root)
avail_moves = root.value[list(root.value)[0]]
cur_move = avail_moves[0]
#print(avail_moves)
self.board.make_move(cur_move, self.color) # Make the optimal move
move = cur_move
return move
def ftu(self, color): #Function to use (min vs max by color)
if color == self.color: # Calculate Min
return max
else: # Calculate Max
return min
def min_max(self, children,
color): # Returns dict -> {Max/min value: Moves to get here}
ftu = self.ftu(color) #Use corresponding min or max depending on color
value_map = {}
for child in children:
for v in child.value.keys():
value_map.setdefault(v, []).append(
child.move
) # D: {heuristic value: Move to make to get here}
# print(value_map)
return {ftu(value_map): value_map[ftu(value_map)]}
def board_points(
self): # 5 + row number for pawns, 5 + row number + 2 for kings
pts = 0
for i in range(self.row):
for j in range(self.col):
checker = self.board.board[i][j]
if checker.color == 'B': # For black side pieces
pts += 5 + checker.row
if checker.is_king: # 2 additional pts for king
pts += 2
elif checker.color == 'W': # FOr white side pieces
pts -= 11 - checker.row # 5 + (6 - Row)
if checker.is_king: # 2 additional pts for king
pts -= 2
return pts if self.color == "B" else -pts
def print_tree(self, root, level=0):
# print("PRINTING TREE")
print("\t" * level, root.value, "->", root.move)
if len(root.children) != 0: # Not Leaf node
for child in root.children:
self.print_tree(child, level + 1)
def rec_tree(self, root: Tree, level=1):
if level == 0:
pass
else:
if root.move is not None: # Not root of tree
self.board.make_move(root.move, root.color)
#Check if win here maybe?
avail_moves = self.board.get_all_possible_moves(
self.opponent[root.color])
for i in range(len(avail_moves)):
for j in range(len(avail_moves[i])):
#print(root)
root.children.append(
Tree(self.opponent[root.color], avail_moves[i][j]))
for child in root.children:
self.rec_tree(child, level - 1)
if root.move is not None:
self.board.undo()
def rec_heuristic(self, root: Tree):
if root.move is not None:
self.board.make_move(root.move, root.color)
if len(root.children) == 0: #Passed node has no children
pass #Evaluate heuristic for board(and return?)
root.value = {self.board_points(): []}
else: #Evaluate rec_heuristic for children, then retrieve values and apply min/max as appropriate
for child in root.children:
self.rec_heuristic(child)
root.value = self.min_max(root.children, root.color)
if root.move is not None:
self.board.undo()
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
self.search_depth = 5
self.debug = True
self.time_used = 0
self.transposition_table = dict()
def get_move(self, move):
if self.debug:
current_move_elapsed = time.time()
if len(move) != 0:
self.board.make_move(move, self.opponent[self.color])
else:
self.color = 1
moves = self.board.get_all_possible_moves(self.color)
# index = randint(0,len(moves)-1)
# inner_index = randint(0,len(moves[index])-1)
# move = moves[index][inner_index]
how_many_moves = 0
for outer_index in range(len(moves)):
how_many_moves += len(moves[outer_index])
if how_many_moves == 1:
if self.debug:
self.time_used += (time.time() - current_move_elapsed)
print("Total elapsed time (in seconds):", self.time_used)
self.board.make_move(moves[0][0], self.color)
return moves[0][0]
depth = round((4 / how_many_moves) + self.search_depth)
if self.debug:
print(how_many_moves, "possible moves")
print("Depth:", depth)
best_move = None
best_move_score = -math.inf
for outer_index in range(len(moves)):
for inner_index in range(len(moves[outer_index])):
self.board.make_move(moves[outer_index][inner_index],
self.color)
move_score = self.search(depth,
moves[outer_index][inner_index],
self.color, -math.inf, math.inf)
self.board.undo()
if move_score > best_move_score:
best_move_score = move_score
best_move = moves[outer_index][inner_index]
self.board.make_move(best_move, self.color)
if self.debug:
self.time_used += (time.time() - current_move_elapsed)
print("Total elapsed time (in seconds):", self.time_used)
return best_move
def search(self, depth, move, turn, alpha, beta):
current_key = self.get_key()
if current_key in self.transposition_table.keys(
) and depth == self.transposition_table[current_key].depth:
return self.transposition_table[current_key].value
winner = self.board.is_win(turn)
win_return = 1000 + depth
if winner != 0:
if winner == -1:
self.transposition_table[current_key] = TTEntry(0, depth)
return 0
if self.color == winner:
self.transposition_table[current_key] = TTEntry(
win_return, depth)
return win_return
self.transposition_table[current_key] = TTEntry(-win_return, depth)
return -win_return
if depth == 0:
black = 0
white = 0
for x in range(self.board.row):
for y in range(self.board.col):
if self.board.board[x][y].color == "W":
white += 5
if self.board.board[x][y].is_king:
white += self.row + 2
else:
white += x
if self.board.board[x][y].color == "B":
black += 5
if self.board.board[x][y].is_king:
black += self.row + 2
else:
black += (self.row - x - 1)
score = black - white
if self.color == 1: # 1 = black
self.transposition_table[current_key] = TTEntry(score, depth)
return score
self.transposition_table[current_key] = TTEntry(-score, depth)
return -score # 2 = white
if turn == self.color: # min
worst = math.inf
possible_moves = self.board.get_all_possible_moves(
self.opponent[self.color])
for x in range(len(possible_moves)):
for y in range(len(possible_moves[x])):
self.board.make_move(possible_moves[x][y],
self.opponent[self.color])
current = self.search(depth - 1, possible_moves[x][y],
self.opponent[self.color], alpha,
beta)
self.board.undo()
if current < worst:
worst = current
if current < beta:
beta = current
if beta <= alpha:
break
else:
continue
break
self.transposition_table[current_key] = TTEntry(worst, depth)
return worst
else: # max
best = -math.inf
possible_moves = self.board.get_all_possible_moves(self.color)
for x in range(len(possible_moves)):
for y in range(len(possible_moves[x])):
self.board.make_move(possible_moves[x][y], self.color)
current = self.search(depth - 1, possible_moves[x][y],
self.color, alpha, beta)
self.board.undo()
if current > best:
best = current
if current > alpha:
alpha = current
if beta <= alpha:
break
else:
continue
break
self.transposition_table[current_key] = TTEntry(best, depth)
return best
def get_key(self):
key = ""
for x in range(self.board.row):
for y in range(self.board.col):
if self.board.board[x][y].color == "B":
key += "1"
elif self.board.board[x][y].color == "W":
key += "2"
else:
key += "0"
return key
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
self.root = Node(self.color, -1)
self.start = None
def flatten(self, ini_list) -> list:
return sum(ini_list, [])
def isTimeLeft(self):
time = datetime.datetime.now()
if (time - self.start).seconds < turnTimer:
return True
return False
def select(
self
) -> Node: #REMINDER: moves is the flattened list of all available moves
maxNode = self.root
maxUct = -1
ptr = self.root
uct = None
found = False
while len(ptr.children) != 0: #Node is not a leaf node
moves = self.flatten(self.board.get_all_possible_moves(ptr.color))
for m in moves:
found = False
for c in ptr.children:
if not found and m == c.move:
uct = c.UCT()
if uct > maxUct:
maxUct = uct
maxNode = c
found = True
if not found:
return ptr #Node is a leaf node, return parent to expand later
if maxNode.move != -1:
self.board.make_move(maxNode.move, ptr.color)
ptr = maxNode
# Node is leaf node
return ptr #Same thing as line 135
def expand(self, node) -> Node:
moves = self.flatten(self.board.get_all_possible_moves(node.color))
toMove = moves[0]
childrenMoves = []
for c in node.children:
childrenMoves.append(c.move.seq)
for m in moves:
if childrenMoves.count(
m.seq
) == 0: #Get all available moves for node, then find the leaf node to expand
toMove = m
break
child = Node(self.opponent[node.color], toMove, node)
node.children.append(child)
return child
def simulate(self, child):
players = {1: "B", 2: "W"}
winner = None
counter = 0
color = child.color
while self.board.is_win(players[color]) == 0:
moves = self.flatten(self.board.get_all_possible_moves(color))
if len(moves) != 0: #player has moves
i = randint(0, len(moves) - 1)
self.board.make_move(moves[i], color)
color = self.opponent[color]
counter += 1
else: #player doesnt have moves, but game hasn't ended yet
color = self.opponent[color]
winner = self.board.is_win(players[color])
while counter != 0:
self.board.undo()
counter -= 1
return winner
def backProp(self, result, child):
while child is not None:
child.upSims()
if result != child.color:
child.upWins()
child = child.parent
def MCTS(self, moves) -> Move:
while (self.isTimeLeft()):
parent = self.select()
expand = self.expand(parent) #TODO check if expand() returns None
result = self.simulate(expand)
self.backProp(result, expand)
bestMove = None # self.root.children[i].move
if len(self.root.children) == 0:
index = randint(0, len(moves) - 1)
bestMove = moves[index]
else:
bestWR = -1
i = 0
while i != len(self.root.children):
if self.root.children[i].getWinRate() > bestWR:
bestWR = self.root.children[i].getWinRate()
bestMove = self.root.children[i].move
i += 1
return bestMove
def get_move(self, move):
if len(move) != 0:
self.board.make_move(move, self.opponent[self.color])
if self.root.parent is None: # len(self.root.children) == 0:
#what if the root.children doesnt contain the one move we wanted?
# FIX: checking len of self.root.children to moves of self.root
self.root.move = move
else:
i = 0
while i != len(self.root.children):
if self.root.children[i].move == move:
break
i += 1
if i != len(self.root.children):
self.root = self.root.children[i]
else: #no child node: add it
new_root = Node(self.color, move, self.root)
self.root.children.append(new_root)
self.root = new_root
else:
self.color = 1
self.root.color = 1
self.start = datetime.datetime.now()
moves = self.flatten(self.board.get_all_possible_moves(
self.root.color))
move = self.MCTS(moves)
self.board.make_move(move,
self.root.color) # PROBLEM LINE: color mismatch
# update root to move just picked from MCTS
i = 0
while i != len(self.root.children):
if self.root.children[i].move == move:
break
i += 1
self.root = self.root.children[i]
return move
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.number_of_move = 0
self.EARLY_GAME = 10
self.MID_GAME = 20
self.END_GAME = 30
self.run_time_depth = 5
self.opponent = {1: 2, 2: 1}
self.color = 2
def get_move(self, move):
if len(move) != 0:
self.board.make_move(move, self.opponent[self.color])
else:
self.color = 1
move = self.best_move()
if self.board.row == self.board.col:
self.run_time_depth = self.get_depth(True)
if self.board.row != self.board.col:
self.run_time_depth = self.get_depth(False)
self.board.make_move(move, self.color)
self.number_of_move += 1
return move
# ALPHA BETA PRUNE STARTS HERE
def best_move(self) -> Move:
moves = self.board.get_all_possible_moves(self.color)
smart_move = Move([])
alpha = -math.inf
beta = math.inf
for checkers in moves:
for move in checkers:
self.board.make_move(move, self.color)
heuristic = self.alpha_beta_prune(1, self.opponent[self.color],
alpha, beta)
self.board.undo()
if heuristic > alpha:
alpha = heuristic
smart_move = Move(move)
return smart_move
def alpha_beta_prune(self, depth, player, alpha, beta) -> float:
all_moves = self.board.get_all_possible_moves(player)
if depth is self.run_time_depth:
return self.evaluate()
if not all_moves:
winplayer = self.board.is_win(self.opponent[player])
if winplayer == self.color:
return 100000
elif winplayer == self.opponent[self.color]:
return -100000
else:
return 0
if player is self.color:
current_score = -math.inf
for checker in all_moves:
for move in checker:
self.board.make_move(move, player)
heuristic = self.alpha_beta_prune(depth + 1,
self.opponent[player],
alpha, beta)
self.board.undo()
current_score = max(heuristic, current_score)
alpha = max(current_score, alpha)
if alpha >= beta:
break
return current_score
else:
current_score = math.inf
for checker in all_moves:
for move in checker:
self.board.make_move(move, player)
heuristic = self.alpha_beta_prune(depth + 1,
self.opponent[player],
alpha, beta)
self.board.undo()
current_score = min(heuristic, current_score)
beta = min(current_score, beta)
if alpha >= beta:
break
return current_score
# make new function to evaluate king
def evaluate(self) -> float:
white_king = 0
white_chess = 0
black_king = 0
black_chess = 0
white_king_list = list()
black_king_list = list()
white_chess_list = list()
black_chess_list = list()
for all_checkers in self.board.board:
for checker in all_checkers:
if checker.is_king:
if checker.color == "W":
white_king += 1
white_king_list.append(checker)
if checker.color == "B":
black_king += 1
black_king_list.append(checker)
else:
if checker.color == "W":
white_chess += 2
white_chess_list.append(checker)
if checker.color == "B":
black_chess += 2
black_chess_list.append(checker)
white_chess, black_chess = self.get_score(
checker, white_chess, black_chess)
king_dis = 1
if self.color == 1:
score = self.board.black_count - self.board.white_count + (
black_king * 8 + black_chess) - (white_chess + white_king * 5)
for checker in black_king_list:
for opponent in white_chess_list:
king_dis += self.calculate_distance(
checker.row, checker.col, opponent.row, opponent.col)
else:
score = 1 + self.board.white_count - self.board.black_count + (
white_king * 8 + white_chess) - (black_chess + black_king * 5)
for checker in white_king_list:
for opponent in black_chess_list:
king_dis += self.calculate_distance(
checker.row, checker.col, opponent.row, opponent.col)
return score / king_dis
def calculate_distance(self, first_row, first_col, second_row,
second_col) -> float:
a = abs(first_row - second_row)
b = abs(first_col - second_col)
return max(a, b)
def get_depth(self, is_equal):
if self.number_of_move != 0:
if is_equal:
if self.number_of_move <= 5:
return 3
if self.number_of_move <= self.EARLY_GAME:
return 5
if self.number_of_move <= self.END_GAME:
return 7
return 3
else:
if 1 <= self.number_of_move <= 5:
return 3
if 6 <= self.number_of_move <= 10:
return 5
if self.number_of_move % 2 == 0:
return 5
return 3
return 3
def get_score(self, checker, white_chess, black_chess):
if checker.col == 0 or checker.col == self.col - 1:
if checker.color == "W":
white_chess += 1
if checker.color == "B":
black_chess += 1
if checker.col - 1 > 0 and checker.row - 1 > 0:
if self.board.board[checker.row - 1][checker.col - 1].color == "W":
white_chess += 0.5
if self.board.board[checker.row - 1][checker.col - 1].color == "B":
black_chess += 0.5
if checker.col - 1 > 0 and checker.row + 1 <= self.row - 1:
if self.board.board[checker.row + 1][checker.col - 1].color == "W":
white_chess += 0.5
if self.board.board[checker.row + 1][checker.col - 1].color == "B":
black_chess += 0.5
if checker.col + 1 <= self.col - 1 and checker.row - 1 > 0:
if self.board.is_in_board(checker.row - 1, checker.col + 1):
if self.board.board[checker.row - 1][checker.col +
1].color == "W":
white_chess += 0.5
if self.board.board[checker.row - 1][checker.col +
1].color == "B":
black_chess += 0.5
if checker.col + 1 < self.col - 1 and checker.row + 1 <= self.row - 1:
if self.board.is_in_board(checker.row + 1, checker.col + 1):
if self.board.board[checker.row + 1][checker.col + 1] == "W":
white_chess += 0.5
if self.board.board[checker.row + 1][checker.col + 1] == "B":
black_chess += 0.5
return white_chess, black_chess
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
def get_move(self, move):
if len(move) != 0:
self.board.make_move(move, self.opponent[self.color])
else:
self.color = 1
moves = self.board.get_all_possible_moves(self.color)
moveValues = []
for i in range(len(moves)):
for j in range(len(moves[i])):
self.board.make_move(moves[i][j], self.color)
moveValues.append((moves[i][j],
self.minimax(self.board, 3,
self.opponent[self.color],
float('-inf'), float('inf'))))
self.board.undo()
move = max(moveValues, key=lambda x: x[1])[0]
self.board.make_move(move, self.color)
return move
def static_eval(self, boardState):
blackValue = 0
whiteValue = 0
for i in range(boardState.row):
for j in range(boardState.col):
checker = boardState.board[i][j]
if checker.color == '.':
continue
elif checker.color == 'B':
if checker.is_king:
blackValue += 7 + boardState.row
else:
blackValue += 5 + checker.row
else:
if checker.is_king:
whiteValue += 7 + boardState.row
else:
whiteValue += 5 + (boardState.row - checker.row)
if self.color == 1:
return blackValue - whiteValue
else:
return whiteValue - blackValue
def generate_children(self, player) -> [Board]:
children = []
checkers = self.board.get_all_possible_moves(player)
for moveList in checkers:
for move in moveList:
boardCopy = deepcopy(self.board)
boardCopy.make_move(move, player)
children.append(boardCopy)
return children
def minimax(self, boardState, depth, max_player, alpha, beta):
if depth == 0 or boardState.is_win(max_player):
return self.static_eval(boardState)
if max_player:
best = float('-inf')
for child in self.generate_children(self.color):
candidate = self.minimax(child, depth - 1, False, alpha, beta)
best = max(best, candidate)
alpha = max(alpha, candidate)
if alpha >= beta:
break
return best
else:
best = float('inf')
for child in self.generate_children(self.opponent[self.color]):
candidate = self.minimax(child, depth - 1, True, alpha, beta)
best = min(best, candidate)
beta = min(beta, candidate)
if alpha >= beta:
break
return best
class StudentAI():
def __init__(self, col, row, p):
self.col = col
self.row = row
self.p = p
self.board = Board(col, row, p)
self.board.initialize_game()
self.color = ''
self.opponent = {1: 2, 2: 1}
self.color = 2
def num_pieces(self, color):
if color == 1:
openent_pieces = self.board.black_count
elif color == 2:
openent_pieces = self.board.white_count
return openent_pieces
def get_max(self, depth, iterations):
opp_color = self.opponent[self.color]
#best = (0,0)
if depth == iterations:
moves = self.board.get_all_possible_moves(self.color)
opponent_pieces = self.num_pieces(
opp_color) #number of opponent pieces before our move
we_eat = 0
#selects max taken pieces
for x, checker in enumerate(moves):
for v, move in enumerate(moves[x]):
self.board.make_move(moves[x][v],
self.color) # we make second move
temp = opponent_pieces - self.num_pieces(opp_color)
if temp > we_eat:
we_eat = temp
#best = (x,v)
self.board.undo()
return (we_eat, 0)
else:
moves = self.board.get_all_possible_moves(self.color)
max = -(self.num_pieces(self.color))
our_pieces = self.num_pieces(
self.color) # our pieces before our move
for i, checker in enumerate(moves):
for j, move in enumerate(moves[i]):
self.board.make_move(moves[i][j], self.color) #Make move
min = self.get_min(depth + 1, iterations)
if min > max:
max = min
best = (i, j)
elif min == max:
if (randint(0, 1)):
best = (i, j)
self.board.undo()
return (max, best)
def get_min(self, depth, iterations):
min_color = self.opponent[self.color]
min = self.num_pieces(min_color)
if depth == iterations - 1:
min_moves = self.board.get_all_possible_moves(min_color)
maxs_pieces = self.num_pieces(self.color)
for x, checker in enumerate(min_moves):
for y, move in enumerate(min_moves[x]):
self.board.make_move(min_moves[x][y], min_color)
min_eats = maxs_pieces - self.num_pieces(self.color)
max_eats, b = self.get_max(depth + 1, iterations)
diff = max_eats - min_eats
if diff < min:
min = diff
self.board.undo()
return min
else:
min_moves = self.board.get_all_possible_moves(min_color)
for x, checker in enumerate(min_moves):
for y, move in enumerate(min_moves[x]):
self.board.make_move(min_moves[x][y], min_color)
max_eats, b = self.get_max(depth + 1, iterations)
if max_eats < min:
min = max_eats
self.board.undo()
return min
def get_move(self, move):
opp_color = self.opponent[self.color]
if len(move) != 0:
self.board.make_move(move, self.opponent[self.color])
else:
self.color = 1
moves = self.board.get_all_possible_moves(self.color)
max, best = self.get_max(1, 3)
'''
if max == 0 and len(self.board.get_all_possible_moves(opp_color)) < 3:
## Idea to improve choice, if max is tied look at the move which will get us closes
## closest to opponent piece
inner = i
distance = 100
checker = self.board.get_all_possible_moves(opp_color)
opp_checker = checker[0][0].seq[0]
#for i,checker in enumerate(moves1):
for j,move in enumerate(moves1[0]):
temp_dist = math.sqrt(sum([(a - b) ** 2 for a, b in zip(move[0], opp_checker)]))
if temp_dist < distance:
best = (0,j)
'''
move = moves[best[0]][best[1]]
self.board.make_move(move, self.color)
return move
