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If anyone is interested in robotics, definitely checkout D-star, which is pretty much a dynamic A-star with changing edge weights.

Dijkstra, A-star and D-star all share a similar structure.

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If you're interested in A* with humanoid robots, check out my post here:

http://victor.hwanger.com/a-technical-peek-into-motion-plann...

Can D-star be used to model problems that have some form of random change, ex: While playing 2048, can you model the new random locations as an opponent making decisions (AI driven)?

In pathological scenarios where the edge weights change near the goal, rather than near the start of the search, D-star and all its backwards-incremental-graph-search variants can be slower than regular A-star. I would only recommend the use of D-star where a) that effect is unlikely or doesn't matter, or b) you can guarantee edge cost changes to occur only near the start of the search - such as a robot with a proprioceptive sensor, which is precisely what the algorithm was designed for.

You can apply probabilties to your edge weights.

Also, if you were considering D, consider looking at D-Lite. It's an alternate approach to the same problem, and more easily understood/implemented.

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