Artificial Intelligence – A* Search Algorithm

In computer science, A* (pronounced as “A star” ( listen)) is a computer algorithm that is widely used in pathfinding and graph traversal, the process of plotting an efficiently directed path between multiple points, called nodes. It enjoys widespread use due to its performance and accuracy. However, in practical travel-routing systems, it is generally outperformed by algorithms which can pre-process the graph to attain better performance,[1] although other work has found A* to be superior to other approaches.[2]

Peter Hart, Nils Nilsson and Bertram Raphael of Stanford Research Institute (now SRI International) first described the algorithm in 1968.[3] It is an extension of Edsger Dijkstra’s 1959 algorithm. A* achieves better performance by using heuristics to guide its search.

Algorithmic Thoughts - Artificial Intelligence | Machine Learning | Neuroscience | Computer Vision

We will try to improve the efficiency of the Uniform Cost Search algorithm by using heuristics which we discussed in the previous post. By improving the efficiency I mean that the algorithm will expand less of the search tree and will give the optimal result faster. We start with the same search problem and search tree that we used for Uniform Cost Search. If you don’t know what search problems are or how search trees are created, visit this post.

A* Search Algorithm

We saw that Uniform Cost Search was optimal in terms of cost for a weighted graph. Now our aim will be to improve the efficiency of the algorithm with the help of heuristics. If you don’t know what heuristics are, visit this post. Particularly, we will be using admissible heuristics for A* Search.

A* Search also makes use of a priority queue just like Uniform…

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