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# Best First Search in Artificial Intelligence

Last Updated on June 29, 2023 by Mayank Dham

Best First Search (BFS) is an artificial intelligence search algorithm that utilizes a priority queue and heuristic search. Its objective is to find the shortest path from an initial state to a goal node in a graph. This algorithm expands graph nodes based on their distance from the starting node, progressing towards the goal node.

## What is the Best First Search?

Best First Search is a search algorithm that incorporates an evaluation function to determine the most promising node for traversal. Unlike uninformed search algorithms, BFS considers the cost associated with each step, prioritizing the most favorable options. By employing a priority queue and heuristic search, BFS efficiently explores the graph space.

Best First Search Algorithm
The Best First Search algorithm follows these steps:

1. Create two empty lists: OPEN and CLOSED.
2. Begin from the initial node (N) and add it to the ordered OPEN list.
3. Repeat the following steps until reaching the goal node:
a. If the OPEN list is empty, exit the loop, returning ‘False’.
b. Select the first/top node (N) from the OPEN list and move it to the CLOSED list. Also, record the parent node information.
c. If N is the goal node, move the node to the Closed list and exit the loop returning ‘True’. The solution can be found by backtracking the path.
d. If N is not the goal node, expand node N to generate immediate next nodes linked to node N and add them to the OPEN list.
e. Reorder the nodes in the OPEN list in ascending order according to an evaluation function f(n).

The Best First Search algorithm prioritizes traversing the shortest path in the queue. Its time complexity is given by O(n*logn).

Variants of Best First Search
There are two notable variants of the Best First Search algorithm

Greedy Best First Search: This variant utilizes a heuristic function and search, enabling a combination of the best aspects of different algorithms.

A Best First Search: In A BFS, the evaluation function incorporates both the heuristic function (h(n)) and the actual distance traveled so far (g(n)). It provides an optimal path while considering the total distance covered.

Best First Search Example
Let’s have a look at the graph below and try to implement both greedy BFS and A* algorithms step by step using the two lists, OPEN and CLOSED.

• Ability to switch between Breadth-First Search (BFS) and Depth-First Search (DFS), gaining the advantages of both.
• More efficient compared to Depth-First Search.

• Increased risk of getting stuck in a loop.

Conclusion
The Best First Search algorithm plays a crucial role in various domains of artificial intelligence, including gaming, puzzle-solving, route optimization, action planning, robotics, and more. By incorporating heuristic evaluation and a priority queue, BFS efficiently finds the shortest path from an initial state to a goal node in a graph. While it has advantages such as efficiency and the ability to combine BFS and DFS, it also has limitations, such as the potential for loop-related issues.

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If you have any doubts or questions, feel free to leave your comments below. Additionally, don’t forget to explore popular free artificial intelligence courses to upskill in the domain.

Q1. What is the purpose of the Best First Search algorithm?
The Best First Search algorithm is used to find the shortest path from a starting node to a goal node in a graph. It prioritizes nodes based on their distance from the starting node and expands them in an ordered manner.

Q2. How does the Best First Search algorithm differ from other search algorithms?
Unlike uninformed search algorithms, Best First Search incorporates an evaluation function that considers the cost associated with each step. This allows it to make informed decisions and prioritize the most promising nodes for traversal.

Q3. What are the variants of Best First Search?
The two notable variants of Best First Search are Greedy Best First Search and A Best First Search. Greedy BFS uses a heuristic function for evaluation, while A BFS considers both the heuristic function and the actual distance traveled so far.

Q4. Is Best First Search an optimal algorithm?
While Greedy Best First Search is not guaranteed to find an optimal solution, A Best First Search ensures optimality by considering the total distance traveled. However, A BFS may require more memory compared to Greedy BFS.

Q5. In which domains or applications is the Best First Search algorithm commonly used?
The Best First Search algorithm finds applications in various domains, including gaming, puzzle-solving, route optimization, action planning, robotics, and more. It is widely used whenever finding the shortest path is crucial for problem-solving.