Difference Between Depth First Search, Breadth First Search and Depth Limit Search in AI

In AI, search algorithms like Depth First Search (DFS), Breadth First Search (BFS), and Depth Limit Search (DLS) are essential for systematically exploring a search space to find solutions. In this article, we'll compare these algorithms, detailing their features, benefits, and uses.

Depth-First Search vs Breadth-First Search vs Depth-Limit Search

The differences between Depth First Search (DFS), Breadth First Search (BFS), and Depth Limit Search (DLS) in AI are as follows:

Depth First Search (DFS):

DFS investigates each branch as far as it can go before turning around. To keep track of which nodes should be visited next, it employs a stack data structure.

This is an implementation in Python:

def dfs(graph, start, visited=None):
    if visited is None:
        visited = set()
    visited.add(start)
    print(start, end=' ')
    for neighbor in graph[start]:
        if neighbor not in visited:
            dfs(graph, neighbor, visited)

# Example usage:
graph = {
    'A': ['B', 'C'],
    'B': ['D', 'E'],
    'C': ['F'],
    'D': [],
    'E': ['F'],
    'F': []
}
print("Depth First Traversal:")
dfs(graph, 'A')

Depth First Traversal:
A B D E F C 

Breadth First Search (BFS):

Before advancing to the nodes at the subsequent depth level, BFS investigates every neighbor node at the current depth. It keeps track of the nodes that will be visited next using a queue data structure.

This is an implementation in Python:

from collections import deque

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

# Example usage:
graph = {
    'A': ['B', 'C'],
    'B': ['D', 'E'],
    'C': ['F'],
    'D': [],
    'E': ['F'],
    'F': []
}
print("Breadth First Traversal:")
bfs(graph, 'A')

Breadth First Traversal:
A B C D E F 

Depth Limited Search (DLS):

While DFS and DLS are comparable, DLS restricts the depth of inquiry. When endless loops in DFS are feasible, it is helpful.

This is an implementation in Python:

def dls(graph, start, depth, visited=None):
    if visited is None:
        visited = set()
    if depth == 0:
        return
    visited.add(start)
    print(start, end=' ')
    for neighbor in graph[start]:
        if neighbor not in visited:
            dls(graph, neighbor, depth-1, visited)

# Example usage:
graph = {
    'A': ['B', 'C'],
    'B': ['D', 'E'],
    'C': ['F'],
    'D': [],
    'E': ['F'],
    'F': []
}
print("Depth Limited Traversal:")
dls(graph, 'A', 2)

Depth Limited Traversal:
A B C 

Conclusions

It is essential to comprehend the differences between DFS, BFS, and DLS in order to choose the best search technique for the given issue requirements. Each method caters to distinct circumstances in AI applications and has its own set of benefits and trade-offs. AI professionals may enhance the effectiveness of their search algorithms by using this comparative study to optimize them.

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Article Tags :

• Artificial Intelligence
• AI-ML-DS
• Algorithms-Searching