Artificial intelligence (AI) is replicating human intelligence using computers. It is accomplished through the acquisition of knowledge and the addition of rules employed by information and then applying these rules to deduce conclusions and self-correction. Several new applications are being developed to make technology more effective and efficient in solving real-world challenges. As a result, numerous training institutions are now offering AI training courses.
Search Algorithms’ Characteristics
Completeness: When a search algorithm returns a solution for any input if at least one solution exists for that input, it is said to be complete.
Optimality: A solution is optimal if the algorithm derives the best solution, i.e., it has the lowest route cost.
Time and space Complexity: time complexity refers to how long it takes an algorithm to finish a task, while space complexity refers to how much storage space is required during the search.
Types of Search Algorithms
- Algorithms for uninformed Search
Search algorithms that aren’t informed do not have any domain knowledge. It is also known as the brute force algorithm because it works in a brute force manner. It does not know how far the goal node is; it knows how to traverse and distinguish between a leaf and a goal node. Furthermore, it investigates each node without prior knowledge, also known as a blind search algorithm.
There are 3 types of uninformed search algorithms:
- Breadth-first search
- Depth-first search
- Uniform cost search
- Algorithms for Informed Search
Domain knowledge is present in informed search algorithms and it includes the problem description and additional information, such as the distance to the goal node. The Heuristic search algorithm is the other name for it. It might not be capable of providing the best solution always, but it will give a good one in a reasonable amount of time. It is highly qualified in solving complicated issues than the uninitiated.
It is mostly divided into two types:
- A* Search: The A* search is a hybrid of greedy and uniform cost searches. The overall cost (heuristic) of this algorithm is indicated by f(x), which is the sum of the cost of uniform cost search g(x) and the cost of greedy search h(x). f (x) = g (x) h (x)
- In this case, g(x) = the backward cost, the total cost from the root node to the current node, and h(x) = the forward cost, which is a rough estimate of the distance between the destination node and the current node.
- GreedyBest First Search: First, the nearest node to the goal node is expanded in this algorithm. Next, the heuristic function h is used to compute the proximity factor (x). When f (n) = h, the node is enlarged or explored (n). The priority queue is used to implement this technique. It isn’t the best algorithm and can become trapped in loops.
Various artificial intelligence search techniques are covered in this article. You can check the best artificial intelligence courses in India from Great Learning to further your career in artificial intelligence and related fields.