启发式搜索Python

启发式搜索（Heuristic Search）是一种基于估价函数的搜索算法，它可以在大规模的状态空间中找到最优解或者近似最优解。在Python中，我们可以使用A*算法来实现启发式搜索。

A*算法是一种启发式搜索算法，它通过估价函数来评估每个状态的优先级，并选择优先级最高的状态进行扩展。估价函数通常是由两部分组成：启发式函数和代价函数。启发式函数用来估计当前状态到目标状态的距离，代价函数用来估计从起始状态到当前状态的代价。

以下是一个简单的A*算法实现：

def astar(start, goal, h_func, cost_func):
    open_set = {start}
    closed_set = set()
    g_score = {start: 0}
    f_score = {start: h_func(start, goal)}
    while open_set:
        current = min(open_set, key=lambda x: f_score[x])
        if current == goal:
            return reconstruct_path(current)
        open_set.remove(current)
        closed_set.add(current)
        for neighbor in get_neighbors(current):
            if neighbor in closed_set:
                continue
            tentative_g_score = g_score[current] + cost_func(current, neighbor)
            if neighbor not in open_set or tentative_g_score < g_score[neighbor]:
                g_score[neighbor] = tentative_g_score
                f_score[neighbor] = tentative_g_score + h_func(neighbor, goal)
                if neighbor not in open_set:
                    open_set.add(neighbor)
    return None

其中，`start`和`goal`分别表示起始状态和目标状态，`h_func`和`cost_func`分别表示启发式函数和代价函数。`get_neighbors`函数用来获取当前状态的所有邻居状态，`reconstruct_path`函数用来重构路径。