8_puzzle.py

This code defines a PuzzleState class to represent each state of the puzzle. It uses the A* search algorithm to find the shortest path from the initial state to the goal state. The Manhattan distance heuristic is used to guide the search. The solve_puzzle function returns the solution state, and the print_solution function prints the sequence of moves to solve the puzzle.

8_puzzle.py

@@ -0,0 +1,92 @@
+from queue import PriorityQueue
+
+class PuzzleState:
+    def __init__(self, board, goal, moves=0, previous=None):
+        self.board = board
+        self.goal = goal
+        self.moves = moves
+        self.previous = previous
+
+    def __lt__(self, other):
+        return self.priority() < other.priority()
+
+    def priority(self):
+        return self.moves + self.manhattan()
+
+    def manhattan(self):
+        distance = 0
+        for i in range(3):
+            for j in range(3):
+                if self.board[i][j] != 0:
+                    x, y = divmod(self.board[i][j] - 1, 3)
+                    distance += abs(x - i) + abs(y - j)
+        return distance
+
+    def is_goal(self):
+        return self.board == self.goal
+
+    def neighbors(self):
+        neighbors = []
+        x, y = next((i, j) for i in range(3) for j in range(3) if self.board[i][j] == 0)
+        directions = [(-1, 0), (1, 0), (0, -1), (0, 1)]
+
+        for dx, dy in directions:
+            nx, ny = x + dx, y + dy
+            if 0 <= nx < 3 and 0 <= ny < 3:
+                new_board = [row[:] for row in self.board]
+                new_board[x][y], new_board[nx][ny] = new_board[nx][ny], new_board[x][y]
+                neighbors.append(PuzzleState(new_board, self.goal, self.moves + 1, self))
+
+        return neighbors
+
+def solve_puzzle(initial_board, goal_board):
+    initial_state = PuzzleState(initial_board, goal_board)
+    frontier = PriorityQueue()
+    frontier.put(initial_state)
+    explored = set()
+
+    while not frontier.empty():
+        current_state = frontier.get()
+
+        if current_state.is_goal():
+            return current_state
+
+        explored.add(tuple(map(tuple, current_state.board)))
+
+        for neighbor in current_state.neighbors():
+            if tuple(map(tuple, neighbor.board)) not in explored:
+                frontier.put(neighbor)
+
+    return None
+
+def print_solution(solution):
+    steps = []
+    while solution:
+        steps.append(solution.board)
+        solution = solution.previous
+    steps.reverse()
+
+    for step in steps:
+        for row in step:
+            print(' '.join(map(str, row)))
+        print()
+
+# Example usage
+initial_board = [
+    [1, 2, 3],
+    [4, 0, 5],
+    [7, 8, 6]
+]
+
+goal_board = [
+    [1, 2, 3],
+    [4, 5, 6],
+    [7, 8, 0]
+]
+
+solution = solve_puzzle(initial_board, goal_board)
+if solution:
+    print("Solution found:")
+    print_solution(solution)
+else:
+    print("No solution found.")