# Popular Search Algorithms in Artificial Intelligence

## 1. Objective

In this blog, we will study Popular Search Algorithms in Artificial Intelligence. Also, we will lesrn all most popular techniques, methods, algorithms and searching techniques. We will use Popular Search Algorithms examples and images for the better understanding.

## 2. Introduction to Popular Search Algorithms in AI

## 3. Single Agent Pathfinding Problems

**For example:**

## 4. Search Algorithms Terminology

**a. Problem Space**

**b. Problem Instance**

**c. Problem Space Graph**

**d. The depth of a problem**

**e. Space Complexity**

**f. Time Complexity**

**g. Admissibility**

**h. Branching Factor**

**i. Depth**

## 5. Brute-Force Search Strategies

**Requirements for Brute Force Algorithms**

## 6. Breadth-First Search Algorithm in AI

**Disadvantage**

- It consumes a lot of memory space. As each level of nodes is saved for creating next one.

- Its complexity depends on the number of nodes. It can check duplicate nodes.

## 7. Depth-First Search Algorithm in AI

**Disadvantage**

- As the algorithm may not terminate and go on infinitely on one path. Hence, a solution to this issue is to choose a cut-off depth.

- If the ideal cut-off is d, and if the chosen cut-off is lesser than d, then this algorithm may fail.

- If the chosen cut-off is more than d, then execution time increases.

- Its complexity depends on the number of paths. It cannot check duplicate nodes.

## 8. Bidirectional Search Algorithm in AI

## 9. Uniform Cost Search Algorithm in AI

- Basically, it performs sorting in increasing cost of the path to a node. Also, always expands the least cost node. Although, it is identical to Breadth-First search if each transition has the same cost.

- It explores paths in the increasing order of cost.

**Disadvantage**

- There can be multiple long paths with the cost ≤ C*.
- Uniform Cost search must explore them all.

## 10. Iterative Deepening Depth-First Search Algorithm

To perform this search we need to follow steps. As it performs the DFS starting to level 1, starts and then executes a complete depth-first search to level 2. Moreover, we have to continue searching process till we find the solution. We have to generate nodes till single nodes are created. Also, it saves only stack of nodes.

As soon as he finds a solution at depth d, the algorithm ends, The number of nodes created at depth d is bd and at depth d-1 is bd-1.

## 11. Informed (Heuristic) Search Strategies Algorithm

### a. Heuristic Evaluation Functions

### b. Pure Heuristic Search

- First, a closed list of the already expanded nodes;

- Secondly, an open list created. Although, unexpected nodes.

## 12. A * Search Algorithm in AI

## 13. Greedy Best First Search Algorithm in AI

**Disadvantage**

- It can get stuck in loops. It is not optimal.

## 14. Local Search Algorithms in AI

### a. Hill-Climbing Search Algorithm in AI

### b. Local Beam Search Algorithm in AI

## 15. Simulated Annealing Algorithm in AI

The process is of heating and cooling a metal to change its internal structure. Although, for modifying its physical properties is known as annealing. As soon as the metal cools, it forms a new structure. Also, metal is going to retain its newly obtained properties. Although, we have to keep the variable temperature in a simulated annealing process.

First, we have to set high temperature. Then, left it to allow “cool” slowly with the proceeding algorithm. Further, if there is high temperature, algorithm accepts worse solutions with high frequency.

## 16. Travelling Salesman Problem

## 17. Conclusion

As a result, we have studied Popular Search Algorithms in AI. Also, will learn methods and Popular Search Algorithms techniques. We have learned its practical uses, so its easier to understand Popular Search Algorithms. Furthermore, if you feel any query to understand Popular Search Algorithms, then feel free to ask in the comment section.