人工智能八数码问题代码

以下是人工智能A*算法完成的八数码问题的Python代码，其中包含完整代码、说明文档和代码解释：

# -*- coding: utf-8 -*-
"""
Created on Fri May  8 16:22:08 2020

@author: yx
"""

import numpy as np
import copy
import heapq

class Node:
    def __init__(self, state, parent, g, h):
        self.state = state
        self.parent = parent
        self.g = g
        self.h = h
        self.f = g + h

    def __lt__(self, other):
        return self.f < other.f

class EightPuzzle:
    def __init__(self, start, goal):
        self.start = start
        self.goal = goal
        self.width = start.shape[0]
        self.actions = [[0, 1], [0, -1], [1, 0], [-1, 0]]

    def manhattan(self, state):
        distance = 0
        for i in range(self.width):
            for j in range(self.width):
                if state[i][j] == 0:
                    continue
                distance += abs(i - (state[i][j] - 1) // self.width) + abs(j - (state[i][j] - 1) % self.width)
        return distance

    def get_successors(self, node):
        successors = []
        zero_pos = np.argwhere(node.state == 0)[0]
        for action in self.actions:
            new_pos = zero_pos + action
            if (new_pos >= self.width).any() or (new_pos < 0).any():
                continue
            new_state = copy.deepcopy(node.state)
            new_state[zero_pos[0]][zero_pos[1]] = new_state[new_pos[0]][new_pos[1]]
            new_state[new_pos[0]][new_pos[1]] = 0
            successors.append(Node(new_state, node, node.g + 1, self.manhattan(new_state)))
        return successors

    def a_star(self):
        start_node = Node(self.start, None, 0, self.manhattan(self.start))
        heap = [start_node]
        visited = set()
        while heap:
            node = heapq.heappop(heap)
            if np.array_equal(node.state, self.goal):
                path = []
                while node:
                    path.append(node.state)
                    node = node.parent
                return path[::-1]
            visited.add(node.state.tobytes())
            successors = self.get_successors(node)
            for successor in successors:
                if successor.state.tobytes() not in visited:
                    heapq.heappush(heap, successor)
        return None

if __name__ == '__main__':
    start = np.array([[2, 8, 3], [1, 6, 4], [7, 0, 5]])
    goal = np.array([[1, 2, 3], [8, 0, 4], [7, 6, 5]])
    puzzle = EightPuzzle(start, goal)
    path = puzzle.a_star()
    print("The solution is:")
    for state in path:
        print(state)

这是一个使用A*算法解决八数码问题的Python代码，其中包含了节点类Node和八数码问题类EightPuzzle。在EightPuzzle类中，manhattan函数计算了当前状态和目标状态之间的曼哈顿距离，get_successors函数返回当前状态的所有后继状态，a_star函数使用A*算法搜索最优解。在主函数中，我们定义了初始状态和目标状态，创建了EightPuzzle对象，并调用a_star函数求解八数码问题。