What are some good methods to finding a heuristic for the A* algorithm?

Question

You have a map of square tiles where you can move in any of the 8 directions. Given that you have function called cost(tile1, tile2) which tells you the cost of

Answer 1

It depends on the cost function.

There are a couple of common heuristics, such as Euclidean distance (the absolute distance between two tiles on a 2d plane) and Manhattan distance (the sum of the absolute x and y deltas). But these assume that the actual cost is never less than a certain amount. Manhattan distance is ruled out if your agent can efficiently move diagonally (i.e. the cost of moving to a diagonal is less than 2). Euclidean distance is ruled out if the cost of moving to a neighbouring tile is less than the absolute distance of that move (e.g. maybe if the adjacent tile was "downhill" from this one).

Edit

Regardless of your cost function, you always have an admissable and consistent heuristic in h(t1, t2) = -∞. It's just not a good one.

Answer 2

Amit's tutorial is one of the best I've seen on A* (Amit's page). You should find some very useful hint about heuristics on this page .

Here is the quote about your problem :

On a square grid that allows 8 directions of movement, use Diagonal distance (L∞).

Answer 3

Yes, the heuristic is dependent on the cost function, in a couple of ways. First, it must be in the same units. Second, you can't have a lower-cost path through actual nodes than the cost of the heuristic.

In the real world, used for things like navigation on a road network, your heuristic might be "the time a car would take on a direct path at 1.5x the speed limit." The cost for each road segment would use the actual speed limit, which will give a higher cost.

So, what is your cost function between tiles? Is it based on physical properties, or defined outside of your graph?