Iterative Deepening Search AI

Iterative Deepening Search (IDS) AI is an uninformed search technique in Artificial Intelligence that combines BFS optimality with DFS memory efficiency. Learn IDS working principle, algorithm steps, time and space complexity, advantages, limitations, and applications.

Iterative Deepening Search AI:

Iterative Deepening Search (IDS)

Introduction

• IDS is an Uninformed Search technique.
• Combines the advantages of BFS and DFS:
  • Optimality like BFS
  • Low memory usage like DFS
• Performs repeated Depth-Limited Search (DLS) with increasing depth limits until the goal is found.

Diagram: IDS Concept

Level-wise Search with Depth Limit Increase

Depth = 0 → A                                      Depth = 1 → A, B, C

Depth = 2 → A, B, D, E, C, F

Goal Found → Stop

Working Principle

1. Start with depth limit = 0
2. Apply Depth-Limited Search (DLS)
3. If the goal is not found → increase depth limit
4. Repeat until the goal node is found

• IDS performs level-by-level search like BFS
• Uses memory efficiently like DFS

IDS Algorithm (Steps)

1. Initialize depth limit = 0
2. Apply Depth-Limited Search (DLS)
3. If goal found → Stop
4. Else, increase depth limit by 1
5. Repeat steps 2–4

Flowchart: IDS Algorithm

Start → Depth = 0

      ↓

Perform Depth-Limited Search

      ↓

Is Goal Found?

      ↓ No → Depth = Depth + 1 → Repeat

      ↓ Yes → Goal Found → Stop

Example of IDS

Tree Structure:

        A

       / \

      B   C

     / \    \

    D   E    F

IDS Traversal Sequence:

• Process stops once the goal node is found.

Time and Space Complexity

Where:

• b = branching factor
• d = depth of the shallowest goal node

IDS uses much less memory than BFS because it performs depth-first search at each iteration.

Characteristics of IDS

• Low memory usage (like DFS)
• Complete and optimal (like BFS)
• Avoids infinite depth problems
• Repeated node expansion occurs

Advantages of IDS

• Memory efficient compared to BFS
• Always provides optimal solution
• Works even on infinite trees
• Combines BFS optimality with DFS memory efficiency

Limitations of IDS

• Repeated node expansions increase time overhead
• Slightly more complex to implement than DFS
• Time consumption is higher than DFS

Applications of IDS

• Game tree search
• Puzzle solving (e.g., 8-puzzle, Sudoku)
• AI planning
• State space search problems
• Memory-constrained systems

Conclusion

Iterative Deepening Search balances optimality and memory efficiency, making it ideal for AI problems where memory is limited but an optimal solution is required.
It is widely used in game trees, puzzle solving, and planning systems.

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
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