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An example of an A* algorithm in action where nodes are cities connected with roads and h(x) is the straight-line distance to the target point: Key: green: start; blue: goal; orange: visited The A* algorithm has real-world applications.
Iterative deepening A* (IDA*) is a graph traversal and path search algorithm that can find the shortest path between a designated start node and any member of a set of goal nodes in a weighted graph. It is a variant of iterative deepening depth-first search that borrows the idea to use a heuristic function to conservatively estimate the ...
Dijkstra's algorithm; A* search algorithm, a special case of the Dijkstra's algorithm; D* a family of incremental heuristic search algorithms for problems in which constraints vary over time or are not completely known when the agent first plans its path
Real-world and many game maps have open areas that are most efficiently traversed in a direct way. Traditional algorithms are ill-equipped to solve these problems: A* with an 8-connected discrete grid graph (2D; 26 for the 3D triple cubic graph) is very fast, but only looks at paths in 45-degree increments. This behavior gives on average 8% ...
The A* search algorithm is an example of a best-first search algorithm, as is B*. Best-first algorithms are often used for path finding in combinatorial search. Neither A* nor B* is a greedy best-first search, as they incorporate the distance from the start in addition to estimated distances to the goal.
In the A* search algorithm, using a consistent heuristic means that once a node is expanded, the cost by which it was reached is the lowest possible, under the same conditions that Dijkstra's algorithm requires in solving the shortest path problem (no negative cost edges).
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The search algorithm uses the admissible heuristic to find an estimated optimal path to the goal state from the current node. For example, in A* search the evaluation function (where is the current node) is: = + where = the evaluation function.