Uninformed	Informed
uninformed search algorithms explore the search space systematically, either by expanding all children of a node (e.g. BFS) or by exploring as deep as possible in a single path before backtracking (e.g. DFS).	It has information about 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.
No additional information provided, just Goal	Heuristics
uninformed search algorithms do not consider the cost of reaching the goal or the likelihood of finding a solution	More efficient as it runs on the greedy algorithm and chooses the best option.
uninformed search algorithms are often simple to implement	It is more focused on the Goal instead of choosing the best path.
uninformed search algorithms can be inefficient in complex problems with large search spaces, leading to an exponential increase in the number of states explored.	informed search algorithms often use cost-based estimates to choose which node should select

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