Compare informed and uninformed search.

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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

Compare informed and uninformed search.

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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