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在这里，我有一个关于 A 星搜索算法的查询。我正在构建所谓的 8 块拼图。这是一个有 9 个位置的游戏（1 个是空的），您必须以正确的顺序排列瓷砖以满足目标位置。

我只需要验证我是否正确编写了算法，这样我就可以在我的代码中寻找问题的其他地方。

我个人认为该算法是正确的，因为它能够解决我创建的第一个预设难题，该难题只需要一次移动即可到达目标位置。但是，它无法解决需要更多动作的谜题。

我试图让它以 3 种不同的方式工作，并且都带来了同样的问题。

尝试1：

while (openList.Count() > 0)
        {
            PuzzleNode currentNode;
            var orderedOpenList = openList.OrderBy(PuzzleNode => PuzzleNode.getPathCost());

            currentNode = orderedOpenList.First();
            if (compare(currentNode.getPuzzle(), goalArray) == true)
            {
                //openList.RemoveAt(0); //POLL
                break;
                // otherwise push currentNode onto closedList & remove from openList
            }
            else
            {
                openList.Remove(currentNode);
                closedList.Add(currentNode);
            }

            //generate all the neighbor nodes
            generateSuccessors(currentNode, tempList);

            for (int i = 0; i < tempList.Count(); i++)
            {
                PuzzleNode tempNode = tempList[i];

                //skip the node if it's in the closed list
                if (closedList.Contains(tempNode))
                {
                    continue;
                }//end if

                //We need to ensure that the G we have seen here is the shortest one
                int gScore = currentNode.getG() + 1;

                if (!openList.Contains(tempNode) || gScore < tempNode.getG())
                {
                    tempNode.setParentNode(currentNode);
                    tempNode.setH(tempNode.calcH(currentHueristic, tempNode.getPuzzle(), goalArray));
                    tempNode.setG(gScore);
                    tempNode.updatePathCost();

                    if (!openList.Contains(tempNode))
                    {
                        openList.Add(tempNode);
                    }//end if


                }//end if
            }//end for

        }//end while

尝试2：

while (openList.Count() > 0)
        {
            PuzzleNode currentNode = GetBestNodeFromOpenList(openList);

            openList.Remove(currentNode);
            closedList.Add(currentNode);

            generateSuccessors(currentNode, tempList);

            foreach (PuzzleNode successorNode in tempList)
            {
                if (compare(successorNode.getPuzzle(), goalArray) == true)
                {
                    //goto thebreak;
                    return successorNode;
                }
                successorNode.setG(currentNode.getG() + 1);
                successorNode.setH(successorNode.calcH(currentHueristic, successorNode.getPuzzle(), goalArray));
                successorNode.updatePathCost();


                if (OpenListHasBetterNode(successorNode, openList))
                    continue;

                openList.Add(successorNode);
            }
        }//end while

private static PuzzleNode GetBestNodeFromOpenList(IEnumerable<PuzzleNode> openList)
    {
        return openList.OrderBy(n => n.getPathCost()).First();
    }

private static bool OpenListHasBetterNode(PuzzleNode successor, IEnumerable<PuzzleNode> list)
    {
        return list.FirstOrDefault(n => n.getG().Equals(successor.getG())
                && n.getPathCost() < successor.getPathCost()) != null;
    }

尝试 2 是我在互联网上找到的算法的更改：解决 8 难题

但是我尽我所能遵循维基百科上的伪代码：

function A*(start,goal)
     closedset := the empty set    // The set of nodes already evaluated.
     openset := {start}    // The set of tentative nodes to be evaluated, initially containing the start node
     came_from := the empty map    // The map of navigated nodes.

     g_score[start] := 0    // Cost from start along best known path.
     // Estimated total cost from start to goal through y.
     f_score[start] := g_score[start] + heuristic_cost_estimate(start, goal)

     while openset is not empty
         current := the node in openset having the lowest f_score[] value
         if current = goal
             return reconstruct_path(came_from, goal)

         remove current from openset
         add current to closedset
         for each neighbor in neighbor_nodes(current)
             tentative_g_score := g_score[current] + dist_between(current,neighbor)
             if neighbor in closedset
                 if tentative_g_score >= g_score[neighbor]
                     continue

             if neighbor not in openset or tentative_g_score < g_score[neighbor] 
                 came_from[neighbor] := current
                 g_score[neighbor] := tentative_g_score
                 f_score[neighbor] := g_score[neighbor] + heuristic_cost_estimate(neighbor, goal)
                 if neighbor not in openset
                     add neighbor to openset

我问你是否能找到一个问题，因为我很困惑为什么它只适用于其中一个谜题。这些难题的唯一区别是解决目标状态所需的移动量。

我已经调试了几个小时，但我看不到它，我也看不到我的代码中的其他地方可能有问题。所以我想我只是在问，这对你来说是否正确？

有任何问题一定要问，我会尽可能提供更多信息！先感谢您！

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1 回答 1

0

注意：我正在使用您可以在此处找到的 CustomPriorityQueue 类（由itowlson编写）

所以在这里我基本上使用 PriorityQueue 它将通过它们的启发式值排列问题状态

这是一个简单的 C# 中的 A* 模板，它说明了 A* 搜索的工作原理：

void enqueueAll(CustomPriorityQueue<PuzzleNode> a, ArrayList<PuzzleNode> b)
{
    foreach(PuzzleNode c in b) a.enqueue(c, h(c));
}

// A* heuritic: h = f + g ; h <= h*
int h(PuzzleNode n);

// returns TRUE if n is a solution
bool isSolution(PuzzleNode n);

// expand n into list of neighbors
ArrayList<PuzzleNode> expand(PuzzleNode n);

PuzzleNode Astar(PuzzleNode startPoint)
{
    CustomPriorityQueue list = new CustomPriorityQueue<PuzzleNode>();
    PuzzleNode current = null;

    list.enqueue(startPoint, h(startPoint));

    while (list.size() > 0)
    {
        current = list.dequeue();

        if (isSolution(current))
        {
            return current;
        }

        enqueueAll(list, expand(current));
    }
}

希望这可以帮助

于 2013-04-24T08:43:17.583 回答