Uninformed AI Search Techniques

In this article Uninformed AI Search Techniques, Uninformed Search, also known as Blind Search, is a fundamental AI search technique that explores the state space without using heuristics. Learn its characteristics, types like BFS, DFS, IDS, UCS, advantages, limitations, and comparison with informed search.

Uninformed AI Search Techniques:

Uninformed Search (Blind Search)

Introduction

Uninformed Search, also known as Blind Search, is a search technique that does not use any additional information (heuristics) about the problem.
It searches for a solution using only the Initial State, Goal State, and State Space.
These techniques do not know which path will reach the goal faster and explore the search space systematically.

Characteristics of Uninformed Search

• Does not use heuristics or domain knowledge
• Explores all possible states
• Simple to implement but consumes more time and memory
• Suitable for small search spaces

Types of Uninformed Search

(i) Breadth-First Search (BFS)

• Starts from the root node
• Explores all nodes at the same level before moving deeper
• Uses a queue (FIFO) data structure
• Complete and optimal (if all step costs are equal)

Time Complexity: O(b^d)
Space Complexity: O(b^d)

(ii) Depth-First Search (DFS)

• Explores nodes depth-wise from a given node
• Uses a stack (LIFO)
• Consumes less memory than BFS
• Not optimal and can get stuck in infinite loops

Time Complexity: O(b^m)
Space Complexity: O(bm)

(iii) Depth-Limited Search (DLS)

• A modified form of DFS with a depth limit (l)
• Prevents infinite loops

Limitation:

• If the target is beyond the depth limit, the solution will not be found

(iv) Iterative Deepening Search (IDS)

• Combines the advantages of BFS and DFS
• Performs DFS with increasing depth limits iteratively
• Reduces memory usage and produces an optimal solution

Time Complexity: O(b^d)
Space Complexity: O(bd)

(v) Uniform Cost Search (UCS)

• Expands the node with the lowest path cost first
• Uses a priority queue
• Suitable for variable step costs
• Complete and optimal

Uninformed AI Search Techniques:

Advantages of Uninformed Search

• Simple and easy to understand
• No additional information required (no heuristics needed)
• Effective for small search spaces

Limitations of Uninformed Search

• Requires more time and memory
• Inefficient for large search spaces
• Explores many unnecessary nodes

Difference Between Uninformed and Informed Search

Conclusion

Uninformed search techniques are suitable for simple and small problems.
However, they require more time and resources for large and complex problems.
Therefore, in modern AI, Informed Search and Evolutionary Search techniques are often preferred for efficiency and scalability.

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