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A* (A-star) search is a popular and widely used algorithm in the field of artificial intelligence and machine learning for pathfinding and graph traversal. It is commonly employed in robotics, video games, and other applications where finding the most efficient path from one point to another is essential. The A* algorithm is an extension of Dijkstra's algorithm with a heuristic component. It is a best-first search algorithm that aims to find the path with the lowest cost, considering both the cost of the path found so far and an estimate of the cost from the current node to the goal. The A* algorithm maintains two main values for each node during the search: 

        g(n): The cost of the path from the start node to node n. 

        h(n): The heuristic estimate of the cost from node n to the goal. 

The total cost for a node is given by,  

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The algorithm explores nodes in order of increasing f(n) value. By doing this, it tends to prioritize nodes that are not only close to the starting point (low g(n)) but also have a low heuristic estimate of the cost to reach the goal (low h(n)). A* has the advantage of being both complete (guaranteed to find a solution if one exists) and optimal (guaranteed to find the lowest-cost solution). However, the efficiency of A* depends heavily on the choice of the heuristic function. If the heuristic is admissible (never overestimates the true cost to the goal), then A* is guaranteed to find the optimal path. If the heuristic is consistent for every node n and successor n' with step cost c, h(n) ≤ h(n') + c, A* can be more efficient in its search.

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