Depth First Search AI

Depth-First Search (DFS) AI is an uninformed search technique in Artificial Intelligence that explores nodes depth-wise using a stack. Learn its algorithm, working principle, time and space complexity, advantages, limitations, and applications.

Depth-First Search (DFS) AI

Introduction

• DFS is an Uninformed Search (Blind Search) technique.
• The search starts from a node and explores as deep as possible along a branch until:
  • The goal is found, or
  • No further nodes remain.
• Backtracking occurs when a dead-end is reached.
• DFS uses a Stack (LIFO – Last In First Out) data structure.

Diagram: DFS Concept

Depth-wise Exploration:

A

/ \

B   C

/ \

D   E

DFS Traversal: A → B → D → E → C

Working Principle

1. Start at the initial node (root).
2. Select a child node to explore first.
3. Continue along the same branch to the deepest node.
4. If a dead end is reached, backtrack to the previous node and explore another branch.
5. Repeat until the goal is found or all nodes are visited.

DFS Algorithm (Steps)

1. Push the starting node into the stack.
2. Pop the top node from the stack.
3. Check if it is the goal node → if yes, stop.
4. If not, push all unvisited child nodes onto the stack.
5. Repeat until the stack is empty.

Flowchart: DFS Algorithm

Start → Push Root to Stack

↓

Is Stack Empty?

↓ No

Pop Top Node → Is Goal?

↓ No → Push unvisited children → Repeat

↓ Yes → Goal Found → Stop

Example of DFS

Tree Structure:

A

/ \

B   C

/ \

D   E

DFS Traversal Sequence:
A → B → D → E → C

Time and Space Complexity

Where:

• b = branching factor (number of children per node)
• m = maximum depth of the tree

DFS uses less memory than BFS because it only stores a single path from the root to the leaf node at a time.

Characteristics of DFS

• Less memory usage
• Simple and easy to implement
• Based on backtracking
• Not optimal
• Can get stuck in infinite depth

Advantages of DFS

• Low space complexity
• Suitable for deep search problems
• Easy to implement

Limitations of DFS

• Does not guarantee an optimal solution
• May enter infinite loops
• Inefficient for large or infinite search spaces

Applications of DFS

• Puzzle solving (e.g., Sudoku)
• Game playing AI
• Maze traversal
• Graph traversal
• Backtracking-based problems

Conclusion

Depth-First Search is a simple and memory-efficient search technique, ideal for deep and small search spaces.
However, due to its lack of optimality and completeness, it is less suitable for complex or large problems.
DFS is often modified as:

• Depth-Limited Search (DLS)
• Iterative Deepening Search (IDS)

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