Searching
more likely to go in the wrong direction than in the right direction. If you keep generating paths like that, you end up with an enormous amount of paths, and even if one of them accidentally reaches the end point, you do not know whether that is the shortest path. So what you want to do is explore directions that are likely to get you to the end point first. On a grid like our map, you can get a rough estimate of how good a path is by checking how long it is and how close its end is to the end point. By adding path length and an estimate of the distance it still has to go, you can get a rough idea of which paths are promising. If you extend promising paths first, you are less likely to waste time on useless ones. ◦•◦ But that still is not enough. If our map was of a perfectly flat plane, the path that looked promising would almost always be the best one, and we could use the above method to walk right to our goal. But we have valleys and hillsides blocking our paths, so it is hard to tell in advance which direction will be the most efficient path. Because of this, we still end up having to explore way too many paths. To correct this, we can make clever use of the fact that we are constantly exploring the most promising path first. Once we have determined that path A is the best way to get to point X, we can remember that. When, later on, path B also gets to point X, we know that it is not the best route, so we do not have to explore it further. This can prevent our program from building a lot of pointless paths.
◦•◦ The algorithm, then, goes something like this… There are two pieces of data to keep track of. The first one is called the open list, it contains the partial routes that must still be explored. Each route has a score, which is calculated by adding its length to its estimated distance from the goal. This estimate must always be optimistic, it should never overestimate the distance. The second is a set of nodes that we have seen, together with the shortest partial route that got us there. This one we will call the reached list. We start by adding a route that contains only the starting node to the open list, and recording it in the reached list. Then, as long as there are any nodes in the open list, we take out the one that has the lowest (best) score, and find the ways in which it can be continued (by calling possibleDirections). For each of the nodes this returns,
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