A* Short Path Algorithm Interactive Task

Finding the shortest route between two places might sound like a simple task, but when a network contains hundreds or even millions of possible paths, computers need an efficient way to decide which route to follow. This is where search algorithms come in.

You may already have explored Dijkstra’s Shortest Path Algorithm, which guarantees finding the shortest path by systematically examining the graph. While Dijkstra’s algorithm is very reliable, it can sometimes spend time investigating routes that clearly lead away from the destination.

The A* (pronounced “A star”) algorithm improves on this idea by introducing a heuristic — an estimate of how far each node is from the destination. Instead of only considering the distance already travelled, A* also takes into account the estimated distance remaining. At each step, it chooses the node with the lowest combined value:

By using this extra information, A* can focus its search towards the target instead of exploring every possible direction. In many real-world situations, this makes it significantly faster than Dijkstra’s algorithm while still guaranteeing the shortest path, provided the heuristic never overestimates the remaining distance.

Because of its efficiency, A* is one of the world’s most widely used pathfinding algorithms. It powers route-planning software, satellite navigation systems, robotics, video games, artificial intelligence, warehouse automation, and many other applications where computers need to navigate efficiently through complex environments.

In this interactive activity, you will practise applying the A* algorithm to a series of weighted directed graphs. For each challenge, use the heuristic values shown on the graph to calculate the total cost for each node, complete the A* table step by step, and determine the shortest path and its total distance. Compare your answers with the worked solution to build confidence in using one of the most important search algorithms in computer science.
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