Thanks faul-sname. I came to the comments to give a much lower effort answer along the same lines, but yours is better.
My answer: lazy local evaluations of nodes surrounding either your current position or the position of the goal. So long as you can estimate a direction from yourself to the goal, there’s no need to embed the whole graph. This is basically gradient descent...
Fun side note: in this particular example, it doesn’t actually matter how you pick your direction. “Choose the axis closest to the target direction” performs exactly as well as “choose any edge which does not make the target node unreachable when traversed at random, and then traverse that edge” or “choose the first edge where traversing that edge does not make the target node unreachable, and traverse that edge”.
Edit: at least assuming that the graph is directed
Thanks faul-sname. I came to the comments to give a much lower effort answer along the same lines, but yours is better. My answer: lazy local evaluations of nodes surrounding either your current position or the position of the goal. So long as you can estimate a direction from yourself to the goal, there’s no need to embed the whole graph. This is basically gradient descent...
Fun side note: in this particular example, it doesn’t actually matter how you pick your direction. “Choose the axis closest to the target direction” performs exactly as well as “choose any edge which does not make the target node unreachable when traversed at random, and then traverse that edge” or “choose the first edge where traversing that edge does not make the target node unreachable, and traverse that edge”.
Edit: at least assuming that the graph is directed