What are the heuristics used in the A* algorithm?
The A* algorithm uses heuristics to estimate the cost from the current node to the goal. Common heuristics include Manhattan distance, Euclidean distance, and Chebyshev distance.
Can you explain how the Manhattan distance heuristic works?
Sure! The Manhattan distance calculates the total grid-based distance between two points by summing the absolute differences of their coordinates. It's useful in grid-like pathfinding.
What are the advantages of using A* over other pathfinding algorithms?
A* is efficient and guarantees the shortest path if the heuristic is admissible. It balances exploration and exploitation, making it faster than algorithms like Dijkstra's in many cases.
Are there any scenarios where A* might not be the best choice?
Yes, A* can be less efficient in very large search spaces or when the heuristic is poorly designed, leading to excessive memory usage or slow performance.
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