# ARTIFICIAL INTELLIGENCE - UNINFORMED SEARCH ALGORITHMS

in Project HOPElast year

Hello Fellas I hope you all are doing well and taking good care of yourselves❤
In one of my earlier posts, i discussed with you all Swarm Intelligence where we saw how the scientists picked points from the nature and invented great algorithms.
Today we are upto the task of going through a Search Algorithm.

SOURCE

The Search algorithms in AI are basically used to determine the optimal path from one place to other, and in technological terminologies; from one node to the goal node.
Where the goal node is said to be the destination.

Just like many other variables in Artificial Intelligence, Search Algorithms also have a huge domain and here we will be discussing some of them.

They can be either Informed Search Algorithms or Uninformed Search Algorithms

Perhaps now you all would be thinking that how can an algorithm be informing or uninforming 😱😉

Lets open it up and move towards the basic definition of one of the type that is the Uninformed Searching Algorithm 👇👇

UNINFORMED SEARCH ALGORITHMS
Some Algorithms in uninformed search differ to other in rules but the basic idea to call them uninformed is because they mostly dont have additonal information other than that is given or provided in the problem.
I am now willing to quote some of the examples of Uninformed search algorithms.

■ BREADTH FIRST SEARCH KNOWN AS BFS
■ DEPTH FIRST SEARCH KNOWN AS DFS
■ UNIFORM COST SEARCH

I am not willing to discuss all of them as it might get a bit boring so lets get to the unorthodox type of Uninformed search Algorithm that is the Uniform Cost Search

Uniform cost search is a bit different from BFS and DFS because here, moving from one node to another has a cost that is known as path cost. And to reach the goal node the path with the least sum is being considered.

Image source

Above is the representation of the Uniform cost search problem where S is the starting node and let G be the GOAL NODE which can be multiple.

The algorithm will go through the graph and find the optimal path to reach goal node in the least value.

In the graph above, the algorithm could find 4 ways to reach the goal state

S -> G (Cost = 12)
S -> A -> C -> G (Cost = 4)
S -> A -> C -> D -> G (Cost = 6)
S -> A -> B -> D -> G (Cost = 10)

In all the above 4 quoted paths, the algorithm choses the most optimal path that costs only 4 path cost.

This is how the Uniform cost search Works.
I hope you understand its value that how effieciently it can save our time and expenses if we know what path is the best path from travelling one place to another😉

Sooner or later, i would surely be discussing the Informed Search Algorithms that are even more Interesting as they include a newer concept of hearistic value😍

I would love to hear from you all what are your views over this❤

I would like to thank the owner of the Project.Hope community @crypto.piotr for managing the community so well and always looking and working to make it stronger❤

The Post is free of Plagiarism and the content is real.
I would request to avoid posting plagiarism in the Project Hope Community🖤

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A proud member of the most loving family❤

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Take great care of yours and your surroundings😘🤗
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