A*算法伪代码答案

【问题标题】：A* algorithm pseudocodeA*算法伪代码
【发布时间】：2016-05-02 04:05:16
【问题描述】：

我从维基百科找到了伪代码

function A*(start, goal)
    // The set of nodes already evaluated.
    closedSet := {}
    // The set of currently discovered nodes still to be evaluated.
    // Initially, only the start node is known.
    openSet := {start}
    // For each node, which node it can most efficiently be reached from.
    // If a node can be reached from many nodes, cameFrom will eventually contain the
    // most efficient previous step.
    cameFrom := the empty map

    // For each node, the cost of getting from the start node to that node.
    gScore := map with default value of Infinity
    // The cost of going from start to start is zero.
    gScore[start] := 0 
    // For each node, the total cost of getting from the start node to the goal
    // by passing by that node. That value is partly known, partly heuristic.
    fScore := map with default value of Infinity
    // For the first node, that value is completely heuristic.
    fScore[start] := heuristic_cost_estimate(start, goal)

    while openSet is not empty
        current := the node in openSet having the lowest fScore[] value
        if current = goal
            return reconstruct_path(cameFrom, current)

        openSet.Remove(current)
        closedSet.Add(current)
        for each neighbor of current
            if neighbor in closedSet
                continue        // Ignore the neighbor which is already evaluated.
            // The distance from start to a neighbor
            tentative_gScore := gScore[current] + dist_between(current, neighbor)
            if neighbor not in openSet  // Discover a new node
                openSet.Add(neighbor)
            else if tentative_gScore >= gScore[neighbor]
                continue        // This is not a better path.

            // This path is the best until now. Record it!
            cameFrom[neighbor] := current
            gScore[neighbor] := tentative_gScore
            fScore[neighbor] := gScore[neighbor] + heuristic_cost_estimate(neighbor, goal)

    return failure

function reconstruct_path(cameFrom, current)
    ....

但我还是不明白，heuristic_cost_estimate()是什么？
伪代码没有显示函数是什么。

在我看来，它是像 dijkstra 这样的其他算法，对吗？

【问题讨论】：

标签： algorithm a-star

【解决方案1】：

该函数将返回一个用于做出决定的启发式值。在 A* 中，它通常是当前节点和最后一个节点之间的最短直线距离，因此函数似乎只是计算两个给定节点之间的距离（直线，不使用路径）。

【讨论】：

所以和dist_between()一样吗？
是的。正如亨利所说，它应该是低于实际值的任何值（使用路径）但为了获得良好的性能，您应该使用节点之间的最短距离
这不是一个“错误”的结果，因为您不知道最短路径，您可以使用此估计来做出决策。如果没有估计（或亨利所说的返回 0），您将是盲目的并尝试随机节点。

【解决方案2】：

启发式必须给出实际成本的下限。重要的是返回值小于或等于实际最小成本，否则算法无法正常工作。

满足此要求的任何估计都将起作用。即使是最简单的选择始终返回 0 也有效。但是，估计越好，算法的性能就越好。

【讨论】：