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Difference between Informed and Uninformed Search in AI

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  • Difficulty Level : Easy
  • Last Updated : 23 Aug, 2022
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Informed Search algorithms have information on the goal state which helps in more efficient searching. This information is obtained by a function that estimates how close a state is to the goal state. Example: Greedy Search and Graph Search.

Uninformed Search algorithms have no additional information on the goal node other than the one provided in the problem definition. The plans to reach the goal state from the start state differ only by the order and length of actions. Examples: Depth First Search and Breadth-First Search.

Pre-requisite: Search Algorithms in Artificial Intelligence 


Solutions Informed Search vs. Uninformed Search is depicted pictorially as follows: 

Parameters Informed Search Uninformed Search
Known as It is also known as Heuristic Search.  It is also known as Blind Search.
Using Knowledge It uses knowledge for the searching process.  It doesn’t use knowledge for the searching process.
Performance It finds a solution more quickly. It finds solution slow as compared to an informed search.
Completion It may or may not be complete. It is always complete.
Cost Factor Cost is low. Cost is high.
Time It consumes less time because of quick searching. It consumes moderate time because of slow searching.
Direction There is a direction given about the solution. No suggestion is given regarding the solution in it.
Implementation It is less lengthy while implemented. It is more lengthy while implemented.
Efficiency It is more efficient as efficiency takes into account cost and performance. The incurred cost is less and speed of finding solutions is quick. It is comparatively less efficient as incurred cost is more and the speed of finding the Breadth-Firstsolution is slow.
Computational requirements Computational requirements are lessened. Comparatively higher computational requirements.
Size of search problems Having a wide scope in terms of handling large search problems. Solving a massive search task is challenging.
Examples of Algorithms
  • Greedy Search
  • A* Search
  • AO* Search
  • Hill Climbing Algorithm
  • Depth First Search (DFS)
  • Breadth First Search (BFS)
  • Branch and Bound
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