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What is the difference between informed and uninformed searches? Can you explain this with some examples?
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Informed SearchThey contain information on goal state. It helps search efficiently. The information is obtained by a function that helps estimate how close a current state is, to the goal state. Examples of informed search include greedy search and graph search. It uses the knowledge in the process of searching.
Using a heuristic, a search strategy can differentiate between non-goal states and focus on those that look more promising. That is why informed search techniques can find the goal faster than an uninformed algorithm, provided that the heuristic function is well-defined.
The informed search algorithm is more useful for large search space. Informed search algorithm uses the idea of heuristic, so it is also called Heuristic search. Heuristics function: Heuristic is a function which is used in Informed Search, and it finds the most promising path.
An informed search (also called "heuristic search") uses prior knowledge about problem ("domain knowledge"), hence possibly more efficient than uninformed search. Examples of informed search algorithms are best-first search and A*.
It is a search without "information" about the goal node.
An example an is breadth-first search (BFS). In BFS, the search proceeds one layer after the other. In other words, nodes in the same layer are first visited before nodes in successive layers. This is performed until a node that is "expanded" is the goal node. In this case, no information about the goal node is used to visit, expand or generate nodes.
We can think of a blind or uniformed search as a brute-force search.
It is a search with "information" about the goal.
An example of such type of algorithm is A*. In this algorithm, nodes are visited and expanded using also information about the goal node. The information about the goal node is given by an heuristic function (which is a function that associates information about the goal node to each of the nodes of the state space). In the case of A*, the heuristic information associated with each node n is an estimate of the distance from n to the goal node.
We can think of a informed search as a approximately "guided" search.
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An uninformed search is a brute-force or "blind" search. It uses no knowledge about problem, hence possibly less efficient than an informed search.
Examples of uninformed search algorithms are breadth-first search, depth-first search, depth-limited search, uniform-cost search, depth-first iterative deepening search and bidirectional search.
An informed search (also called "heuristic search") uses prior knowledge about problem ("domain knowledge"), hence possibly more efficient than uninformed search.
Examples of informed search algorithms are best-first search and A*.
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