What is A* search, and how is it different from Dijkstra’s algorithm?
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A* (A-star) search is a pathfinding and graph traversal algorithm widely used in AI for finding the shortest path between nodes. It combines the strengths of Dijkstra’s algorithm and Greedy Best-First Search by considering both the actual cost to reach a node and the estimated cost to the goal.
A* uses the evaluation function:
f(n) = g(n) + h(n)
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g(n): actual cost from start to node n.
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h(n): heuristic estimate of cost from n to goal.
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f(n): total estimated cost of the path through n.
If the heuristic function h(n) is admissible (never overestimates) and consistent, A* guarantees the shortest path.
Difference from Dijkstra’s Algorithm:
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Dijkstra’s explores nodes based only on the shortest known distance from the start (g(n)), without using a heuristic. This makes it optimal but slower, as it explores many unnecessary nodes.
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A* adds the heuristic h(n), guiding the search toward the goal and reducing explored paths, making it faster and more efficient in practice.
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If h(n) = 0, A* behaves exactly like Dijkstra’s.
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If h(n) is too aggressive (overestimates), A* may lose optimality.
In summary: Dijkstra’s guarantees shortest paths by exploring broadly, while A* balances cost and heuristic to reach the goal faster, making it ideal for AI tasks like route planning, games, and robotics.
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