BFS search algorithm | uninformed | Artificial intelligence | Lec-12 | Bhanu Priya

Introduction

This tutorial provides a step-by-step guide to understanding and implementing the Breadth-First Search (BFS) algorithm in artificial intelligence. BFS is an uninformed search strategy used to explore the nodes and edges of a graph systematically, making it essential for pathfinding and graph traversal tasks.

Step 1: Understand the Basics of BFS

• Definition: BFS is a graph traversal algorithm that explores vertices in layers, starting from a source node and moving outward.
• Characteristics:
  • Explores all neighbor nodes at the present depth before moving on to nodes at the next depth level.
  • Guarantees the shortest path in an unweighted graph.

Step 2: Set Up the Graph Structure

• Graph Representation: Represent the graph using an adjacency list or matrix. Adjacency lists are often more space-efficient.
• Example of an Adjacency List:

  graph = {
      'A': ['B', 'C'],
      'B': ['D', 'E'],
      'C': ['F'],
      'D': [],
      'E': ['F'],
      'F': []
  }
  

Step 3: Implement the BFS Algorithm

• Initialization:
  • Create a queue to keep track of nodes to explore.
  • Use a set to track visited nodes.
• Algorithm Steps:
  1. Enqueue the starting node and mark it as visited.
  2. While the queue is not empty:
     • Dequeue a node.
     • Process the node (e.g., print or store it).
     • Enqueue all unvisited neighbors of the node and mark them as visited.

Example Code Implementation

from collections import deque

def bfs(graph, start):
    visited = set()
    queue = deque([start])
    
    while queue:
        vertex = queue.popleft()
        print(vertex)
        visited.add(vertex)
        
        for neighbor in graph[vertex]:
            if neighbor not in visited:
                visited.add(neighbor)
                queue.append(neighbor)

# Running the BFS
bfs(graph, 'A')

Step 4: Analyze the Time and Space Complexity

• Time Complexity: O(V + E), where V is the number of vertices and E is the number of edges.
• Space Complexity: O(V) for storing the queue and the set of visited nodes.

Step 5: Explore Practical Applications

• BFS is commonly used in:
  • Shortest path algorithms.
  • Social networking applications to find connections.
  • Web crawling to explore links on a website.

Conclusion

In this tutorial, we covered the fundamentals of the BFS algorithm, how to implement it in Python, and its applications in various fields. Understanding BFS is crucial for solving problems related to pathfinding and graph traversal. Next, you may want to explore variations of BFS, such as Bidirectional BFS, or practice solving graph-related problems to deepen your understanding.