search

nickumia / cap6635

A summary of the AI techniques explored in Dr. Zhu's AI class
GNU General Public License v3.0
0 stars 0 forks source link

Add Minimax + AlphaBeta TicTacToe #4

Closed nickumia closed 1 year ago

nickumia commented 1 year ago 
# CAP 6635 Artificial Intelligence
# Min_Max Decision playing Tic-Tac-Toe
# X. Zhu, June 28 2023
# Code adapted from: https://stackabuse.com/minimax-and-alpha-beta-pruning-in-python/
Minimax ```python import time class Game: def __init__(self): self.initialize_game() def initialize_game(self): self.current_state = [['.','.','.'], ['.','.','.'], ['.','.','.']] # Player X always plays first self.player_turn = 'X' def draw_board(self): for i in range(0, 3): for j in range(0, 3): print('{}|'.format(self.current_state[i][j]), end=" ") print() print() # Determines if the made move is a legal move def is_valid(self, px, py): if px < 0 or px > 2 or py < 0 or py > 2: return False elif self.current_state[px][py] != '.': return False else: return True # Checks if the game has ended and returns the winner in each case def is_end(self): # Vertical win for i in range(0, 3): if (self.current_state[0][i] != '.' and self.current_state[0][i] == self.current_state[1][i] and self.current_state[1][i] == self.current_state[2][i]): return self.current_state[0][i] # Horizontal win for i in range(0, 3): if (self.current_state[i] == ['X', 'X', 'X']): return 'X' elif (self.current_state[i] == ['O', 'O', 'O']): return 'O' # Main diagonal win if (self.current_state[0][0] != '.' and self.current_state[0][0] == self.current_state[1][1] and self.current_state[0][0] == self.current_state[2][2]): return self.current_state[0][0] # Second diagonal win if (self.current_state[0][2] != '.' and self.current_state[0][2] == self.current_state[1][1] and self.current_state[0][2] == self.current_state[2][0]): return self.current_state[0][2] # Is whole board full? for i in range(0, 3): for j in range(0, 3): # There's an empty field, we continue the game if (self.current_state[i][j] == '.'): return None # It's a tie! return '.' # Player 'O' is max, in this case AI def max(self): # Possible values for maxv are: # -1 - loss # 0 - a tie # 1 - win # We're initially setting it to -2 as worse than the worst case: maxv = -2 px = None py = None result = self.is_end() # If the game came to an end, the function needs to return # the evaluation function of the end. That can be: # -1 - loss # 0 - a tie # 1 - win if result == 'X': return (-1, 0, 0) elif result == 'O': return (1, 0, 0) elif result == '.': return (0, 0, 0) for i in range(0, 3): for j in range(0, 3): if self.current_state[i][j] == '.': # On the empty field player 'O' makes a move and calls Min # That's one branch of the game tree. self.current_state[i][j] = 'O' (m, min_i, min_j) = self.min() # Fixing the maxv value if needed if m > maxv: maxv = m px = i py = j # Setting back the field to empty self.current_state[i][j] = '.' return (maxv, px, py) # Player 'X' is min, in this case human def min(self): # Possible values for minv are: # -1 - win # 0 - a tie # 1 - loss # We're initially setting it to 2 as worse than the worst case: minv = 2 qx = None qy = None result = self.is_end() if result == 'X': return (-1, 0, 0) elif result == 'O': return (1, 0, 0) elif result == '.': return (0, 0, 0) for i in range(0, 3): for j in range(0, 3): if self.current_state[i][j] == '.': self.current_state[i][j] = 'X' (m, max_i, max_j) = self.max() if m < minv: minv = m qx = i qy = j self.current_state[i][j] = '.' return (minv, qx, qy) def play(self): totalTime=0 while True: self.draw_board() self.result = self.is_end() # Printing the appropriate message if the game has ended if self.result != None: if self.result == 'X': print('The winner is X!') elif self.result == 'O': print('The winner is O!') elif self.result == '.': print("It's a tie!") self.initialize_game() print('The total Evaluation time:{}s'.format(round(totalTime, 7))) return # If it's player's turn if self.player_turn == 'X': while True: start = time.time() (m, qx, qy) = self.min() end = time.time() totalTime=totalTime+(end-start) print('Evaluation time: {}s'.format(round(end - start, 7))) print('Recommended move: X = {}, Y = {}'.format(qx, qy)) px = int(input('Insert the X coordinate: ')) py = int(input('Insert the Y coordinate: ')) (qx, qy) = (px, py) if self.is_valid(px, py): self.current_state[px][py] = 'X' self.player_turn = 'O' break else: print('The move is not valid! Try again.') # If it's AI's turn else: (m, px, py) = self.max() self.current_state[px][py] = 'O' self.player_turn = 'X' ```
Alpha-Beta Pruning ```python import time class Game: def __init__(self): self.initialize_game() def initialize_game(self): self.current_state = [['.','.','.'], ['.','.','.'], ['.','.','.']] # Player X always plays first self.player_turn = 'X' def draw_board(self): for i in range(0, 3): for j in range(0, 3): print('{}|'.format(self.current_state[i][j]), end=" ") print() print() # Determines if the made move is a legal move def is_valid(self, px, py): if px < 0 or px > 2 or py < 0 or py > 2: return False elif self.current_state[px][py] != '.': return False else: return True # Checks if the game has ended and returns the winner in each case def is_end(self): # Vertical win for i in range(0, 3): if (self.current_state[0][i] != '.' and self.current_state[0][i] == self.current_state[1][i] and self.current_state[1][i] == self.current_state[2][i]): return self.current_state[0][i] # Horizontal win for i in range(0, 3): if (self.current_state[i] == ['X', 'X', 'X']): return 'X' elif (self.current_state[i] == ['O', 'O', 'O']): return 'O' # Main diagonal win if (self.current_state[0][0] != '.' and self.current_state[0][0] == self.current_state[1][1] and self.current_state[0][0] == self.current_state[2][2]): return self.current_state[0][0] # Second diagonal win if (self.current_state[0][2] != '.' and self.current_state[0][2] == self.current_state[1][1] and self.current_state[0][2] == self.current_state[2][0]): return self.current_state[0][2] # Is whole board full? for i in range(0, 3): for j in range(0, 3): # There's an empty field, we continue the game if (self.current_state[i][j] == '.'): return None # It's a tie! return '.' # Player 'O' is max, in this case AI def max_alpha_beta(self, alpha, beta): maxv = -2 px = None py = None result = self.is_end() if result == 'X': return (-1, 0, 0) elif result == 'O': return (1, 0, 0) elif result == '.': return (0, 0, 0) for i in range(0, 3): for j in range(0, 3): if self.current_state[i][j] == '.': self.current_state[i][j] = 'O' (m, min_i, in_j) = self.min_alpha_beta(alpha, beta) if m > maxv: maxv = m px = i py = j self.current_state[i][j] = '.' # Next two ifs in Max and Min are the only difference between regular algorithm and minimax if maxv >= beta: return (maxv, px, py) if maxv > alpha: alpha = maxv return (maxv, px, py) # Player 'X' is min, in this case human def min_alpha_beta(self, alpha, beta): minv = 2 qx = None qy = None result = self.is_end() if result == 'X': return (-1, 0, 0) elif result == 'O': return (1, 0, 0) elif result == '.': return (0, 0, 0) for i in range(0, 3): for j in range(0, 3): if self.current_state[i][j] == '.': self.current_state[i][j] = 'X' (m, max_i, max_j) = self.max_alpha_beta(alpha, beta) if m < minv: minv = m qx = i qy = j self.current_state[i][j] = '.' if minv <= alpha: return (minv, qx, qy) if minv < beta: beta = minv return (minv, qx, qy) def play_alpha_beta(self): totalTime=0 while True: self.draw_board() self.result = self.is_end() if self.result != None: if self.result == 'X': print('The winner is X!') elif self.result == 'O': print('The winner is O!') elif self.result == '.': print("It's a tie!") self.initialize_game() print('The total Evaluation time:{}s'.format(round(totalTime, 7))) return if self.player_turn == 'X': while True: start = time.time() (m, qx, qy) = self.min_alpha_beta(-2, 2) end = time.time() totalTime=totalTime+(end-start) print('Evaluation time: {}s'.format(round(end - start, 7))) print('Recommended move: X = {}, Y = {}'.format(qx, qy)) px = int(input('Insert the X coordinate: ')) py = int(input('Insert the Y coordinate: ')) qx = px qy = py if self.is_valid(px, py): self.current_state[px][py] = 'X' self.player_turn = 'O' break else: print('The move is not valid! Try again.') else: (m, px, py) = self.max_alpha_beta(-2, 2) self.current_state[px][py] = 'O' self.player_turn = 'X' ```
nickumia commented 1 year ago

Random notes:

def max/min():
  # Define the worst case
  # Check if the final result is reached
  # For every possible move, fake move and pass to min/max()
  # Return the best answer

def main():
  # init board
  # input human move
  # calculate computer move
  # iterate until final result is reached

More to come..

nickumia commented 1 year ago

Finally got some traction on this. I think the code needs to be organized a bit and documented more, but this is good enough for now. Maybe future work: