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Astart_8_Puzzle_Problem.py
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Astart_8_Puzzle_Problem.py
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import heapq
class PuzzleNode:
def _init_(self, state, parent=None, move=None):
self.state = state
self.parent = parent
self.move = move
self.depth = 0
if parent:
self.depth = parent.depth + 1
def _eq_(self, other):
return self.state == other.state
def _lt_(self, other):
return (self.depth + self.heuristic()) < (other.depth + other.heuristic())
def _hash_(self):
return hash(str(self.state))
def is_goal(self, goal_state):
return self.state == goal_state
def heuristic(self):
distance = 0
for i in range(3):
for j in range(3):
if self.state[i][j] != 0:
row, col = divmod(self.state[i][j] - 1, 3)
distance += abs(i - row) + abs(j - col)
return distance
def get_possible_moves(self):
possible_moves = []
empty_row, empty_col = self.find_empty_tile()
moves = [(0, 1), (0, -1), (1, 0), (-1, 0)]
for dr, dc in moves:
new_row, new_col = empty_row + dr, empty_col + dc
if 0 <= new_row < 3 and 0 <= new_col < 3:
new_state = [row[:] for row in self.state]
new_state[empty_row][empty_col], new_state[new_row][new_col] = \
new_state[new_row][new_col], new_state[empty_row][empty_col]
possible_moves.append((new_state, (new_row, new_col)))
return possible_moves
def find_empty_tile(self):
for i in range(3):
for j in range(3):
if self.state[i][j] == 0:
return i, j
def reconstruct_path(node):
path = []
while node.parent:
path.append(node.move)
node = node.parent
path.reverse()
return path
def solve_8_puzzle(initial_state, goal_state):
open_set = [PuzzleNode(initial_state)]
closed_set = set()
while open_set:
current_node = heapq.heappop(open_set)
if current_node.is_goal(goal_state):
return reconstruct_path(current_node)
closed_set.add(current_node)
for move_state, move in current_node.get_possible_moves():
child = PuzzleNode(move_state, current_node, move)
if child in closed_set:
continue
if child not in open_set:
heapq.heappush(open_set, child)
else:
existing_node = open_set[open_set.index(child)]
if child.depth < existing_node.depth:
existing_node.parent = current_node
existing_node.depth = child.depth
return None
initial_state = [[1, 2, 3], [4, 5, 6], [0, 7, 8]]
goal_state = [[1, 2, 3], [4, 5, 6], [7, 8, 0]]
solution = solve_8_puzzle(initial_state, goal_state)
if solution:
print("Solution found in {} steps:".format(len(solution)))
for step, move in enumerate(solution, 1):
print("Step {}: Move empty tile to {}".format(step, move))
else:
print("No solution found.")