BFS, DFS and UCS Beginners guide
It's amazing how the term "searching" can be applied to many difference problems we have on our hand. Searching, is one of the most important activity that comes under different names. Query, find, detect, check ...etc. All these words can be taken under the term searching. Often times running a search is an easier task. But implementing a search is not. Think about a relational database table.…
Searching is a crucial activity that manifests in various forms, such as query, find, detect, and check. While running a search is often straightforward, implementing one can be challenging. Consider a relational database table, where searching for a record using SQL is relatively simple. However, implementing this search within a database management system may prove complex.
An example of searching is finding an item within an array, a task we frequently perform using our language or library. On occasion, however, searching can be more intricate and generalized. For instance, determining the optimal move in a finite amount of time during a chess game requires this type of search problem.
Three widely applicable searching algorithms under the umbrella of uninformed search in artificial intelligence are Breadth First Search, Depth First Search, and Uniform Cost Search. Breadth First Search begins by identifying the source, which could be a table, graph, or collection of files. The algorithm then requires a starting point and a target for the search. To ensure the search's success, the target must be part of the source; otherwise, the search will fail.
Breadth First Search entails an expansive search that thoroughly covers the entire source.
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