In order to understand A*, you’ll need to be comfortable Dijkstra’s Algorithm first!
A* Algorithm #
The A* Search Algorithm is incredibly similar to Dijkstra’s Algorithm with one addition: a heuristic function.
This heuristic function calculates weights of a path from a vertex to a goal vertex. This way, we can help bias our algorithm in the right direction so that it doesn’t make a bunch of bad moves.
This has an important implication: not all vertices get visited. The algorithm only cares about finding the best path to the goal, and not any other vertex (assuming we design our heuristic well).
The order that the vertices get visited is lowest distance + heuristic. This is basically the same as Dijkstra’s, just with that added heuristic term.
What’s a good heuristic? #
Heuristic functions can be really tricky to design, since there isn’t much to go off of.
A good heuristic has these two properties:
- Admissible - heuristic of each vertex returns a cost that is <= the true cost/distance i.e. h(A) <= cost(A, goal)
- Consistent - difference between heuristics of two vertices <= true cost between them i.e. h(A) - h(B) <= cost(A, B)