Breadth First Search AI

Breadth-First Search (BFS) AI is an uninformed search technique in Artificial Intelligence that explores nodes level by level using a queue. Learn BFS algorithm steps, working principle, time and space complexity, advantages, limitations, and real-world applications.

Breadth First Search AI

Breadth-First Search (BFS)

Introduction

• BFS is an Uninformed Search (Blind Search) technique.
• The search starts from the root node and proceeds level by level.
• Nearest nodes are explored first, followed by nodes of the next level.
• BFS uses a Queue (FIFO – First In First Out) data structure.

Diagram: BFS Concept

Level-wise Exploration:

        A

       / \

      B   C

     / \   \

    D   E   F

BFS Traversal: A → B → C → D → E → F

Working Principle

• Start from the initial state.
• Expand all nodes of the same level first.
• Move to the next level nodes.
• Continue until the goal node is found.
• Use a queue (FIFO) to manage nodes.

BFS Algorithm (Steps)

• Insert the starting node into the queue.
• Remove the front node from the queue.
• Check if the node is the goal → if yes, stop.
• If not, add all unvisited child nodes to the queue.
• Repeat until the queue is empty.

Flowchart: BFS Algorithm

Start → Initialize Queue with Root

      ↓

Is Queue Empty?

      ↓ No

Dequeue Node → Is Goal Node?

      ↓ No → Enqueue unvisited children → Repeat

      ↓ Yes → Goal Found → Stop

Example of BFS

Tree Structure:

        A

       / \

      B   C

     / \   \

    D   E   F

BFS Traversal Sequence:
A → B → C → D → E → F

Time and Space Complexity

Where:

• b = branching factor (number of children per node)
• d = depth of the shallowest goal node

Characteristics of BFS

• Level-wise search
• Complete (if branching factor is finite)
• Optimal (if all step costs are equal)
• Requires more memory than DFS

Advantages of BFS

• Always finds the shortest path to the goal
• Optimal solution when step costs are equal
• Simple, systematic, and predictable

Limitations of BFS

• High memory usage
• Inefficient for large search spaces
• Performance degrades with high branching factor

Applications of BFS

• Shortest path problems (e.g., GPS routing)
• Web crawling
• Network broadcasting
• Puzzle solving (minimum moves)
• AI planning systems

Conclusion

Breadth-First Search is a complete and optimal search technique, ideal for small to medium search spaces.
However, due to its high space complexity, it may not be suitable for large problems.
In such cases, Depth-First Search (DFS) or Iterative Deepening Search (IDS) are preferred.

Some More: 

• POP- Introduction to Programming Using ‘C’
• DS – Data structure Using C
• OOP – Object Oriented Programming 
• Java Programming
• DBMS – Database Management System
• RDBMS – Relational Database Management System
• https://defineinfoloop.blogspot.com/?m=1