Implement Alpha-beta pruning of Minimax Search Algorithm for a Simple TIC-TAC-TOE game
Improve the decision-making efficiency of the computer player by reducing the number of evaluated nodes in the game tree.
Tic-Tac-Toe game implementation incorporating the Alpha-Beta pruning and the Minimax algorithm with Python Code.
The project involves developing a Tic-Tac-Toe game implementation incorporating the Alpha-Beta pruning with the Minimax algorithm. Using this algorithm, the computer player analyzes the game state, evaluates possible moves, and selects the optimal action based on the anticipated outcomes.
recursively evaluates all possible moves and their potential outcomes, creating a game tree.
Alpha–Beta (𝛼−𝛽) algorithm is actually an improved minimax using a heuristic. It stops evaluating a move when it makes sure that it’s worse than a previously examined move. Such moves need not to be evaluated further.
When added to a simple minimax algorithm, it gives the same output but cuts off certain branches that can’t possibly affect the final decision — dramatically improving the performance
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()
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
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 the 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 '.'
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)
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):
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()
return
if self.player_turn == 'X':
while True:
start = time.time()
(m, qx, qy) = self.min_alpha_beta(-2, 2)
end = time.time()
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'
def main():
g = Game()
g.play_alpha_beta()
if __name__ == "__main__":
main()
Thus,Implement Alpha-beta pruning of Minimax Search Algorithm for a Simple TIC-TAC-TOE game was done successfully.
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