Editing A* search algorithm (section)

==Relations to other algorithms==
What sets A* apart from a [[greedy algorithm|greedy]] best-first search algorithm is that it takes the cost/distance already traveled, {{math|''g''(''n'')}}, into account.

Some common variants of [[Dijkstra's algorithm]] can be viewed as a special case of A* where the heuristic <math>h(n) = 0</math> for all nodes;<ref name="geospatial"/><ref name="pythalgs"/> in turn, both Dijkstra and A* are special cases of [[dynamic programming]].<ref>{{cite conference |first1=Dave |last1=Ferguson |first2=Maxim |last2=Likhachev |first3=Anthony |last3=Stentz |title=A Guide to Heuristic-based Path Planning |year=2005 |url=https://www.cs.cmu.edu/afs/cs.cmu.edu/Web/People/maxim/files/hsplanguide_icaps05ws.pdf |archive-url=https://web.archive.org/web/20160629214339/https://www.cs.cmu.edu/afs/cs.cmu.edu/Web/People/maxim/files/hsplanguide_icaps05ws.pdf |archive-date=2016-06-29 |url-status=live |book-title=Proceedings of the international workshop on planning under uncertainty for autonomous systems, international conference on automated planning and scheduling (ICAPS) |pages=9–18}}</ref>
A* itself is a special case of a generalization of [[branch and bound]].<ref>{{cite journal |last1=Nau |first1=Dana S. |first2=Vipin |last2=Kumar |first3=Laveen |last3=Kanal |title=General branch and bound, and its relation to A∗ and AO∗ |journal=Artificial Intelligence |volume=23 |issue=1 |year=1984 |pages=29–58 |url=https://www.cs.umd.edu/~nau/papers/nau1984general.pdf |archive-url=https://web.archive.org/web/20121004051632/http://www.cs.umd.edu/~nau/papers/nau1984general.pdf |archive-date=2012-10-04 |url-status=live |doi=10.1016/0004-3702(84)90004-3}}</ref>

A* is similar to [[beam search]] except that beam search maintains a limit on the numbers of paths that it has to explore.<ref>{{Cite web |title=Variants of A* |url=http://theory.stanford.edu/~amitp/GameProgramming/Variations.html |access-date=2023-06-09 |website=theory.stanford.edu}}</ref>