Informed vs uninformed Search: Know the Difference between Informed and Uninformed Search in AI
Searching is the process in which a machine/AI finds the sequence of various steps required to solve the given problem. It is of two major types, informed and uninformed. There is a primary difference between informed and uninformed search in AI. On the one hand, primary search equips the AI with guidance regarding how and where it can find the problem’s solution. Conversely, an uninformed search, as the name suggests, provides no additional info to the AI regarding the problem’s solution. It only provides the AI with its specification.
Out of both, the informed search provides more efficiency to an AI, and it costs less. In this article, we will look at the fundamental difference between informed and uninformed search in AI. But before we do that, let us know a bit more about each of these in detail.
What is an Informed Search in AI?
The algorithms of an informed search contain information regarding the goal state. It helps an AI make more efficient and accurate searches. A function obtains this data/info to estimate the closeness of a state to its goal in the system.
For example, Graph Search and Greedy Search.
Features of Informed Search in AI:
- It consists of information regarding the goal state.
- It makes a search more efficient.
- A function obtains the data/info regarding the closeness of the current state of a search to its goal state.
- It utilises knowledge for implementing the searching process.
- A few examples include graph search and greedy search.
- It incurs less cost.
- It may be complete or incomplete.
- A solution can be found much quicker.
- This type of search consumes less time.
- Implementation of such an AI is short and quick, not at all lengthy.
- The AI gets a direct suggestion about the solution of the search/ problem.
What is an Uninformed Search in AI?
The algorithms of a uniformed AI do not consist of any additional data/ info regarding the goal node. It only contains the information provided during the definition of a problem. The plans required to reach from the start state to the goal state differ only on the basis of the length and order of the provided actions.
For example, Breadth-First Search and Depth-First Search.
Features of Uninformed Search in AI:
- It does not contain any additional data/ info.
- The information is provided to the AI during the definition of a problem.
- It can reach the goal state on the basis of the length and the order of actions performed.
- The AI doesn’t utilise any knowledge to search for the solution to a problem.
- A few examples of these include BFS (Breadth-First Search) and DFS (Depth-First Search).
- This type of AI takes more time to generate a solution for any problem.
- It is always complete.
- The total cost incurred is generally more than that of Informed Search in AI.
- It consumes a fairly moderate time to do the search.
- The implementation is lengthy.
- No suggestion is present for finding any solution.
Difference between Informed and Uninformed Search in AI
Let us talk about the differences between Informed and Uninformed Search in AI.
| Parameters | Informed Search | Uninformed Search |
| Utilizing Knowledge | It uses knowledge during the process of searching. | It does not require using any knowledge during the process of searching. |
| Speed | Finding the solution is quicker. | Finding the solution is much slower comparatively. |
| Completion | It can be both complete and incomplete. | It is always bound to be complete. |
| Consumption of Time | Due to a quicker search, it consumes much less time. | Due to slow searches, it consumes comparatively more time. |
| Cost Incurred | The expenses are much lower. | The expenses are comparatively higher. |
| Suggestion/ Direction | The AI gets suggestions regarding how and where to find a solution to any problem. | The AI does not get any suggestions regarding what solution to find and where to find it. Whatever knowledge it gets is out of the information provided. |
| Efficiency | It costs less and generates quicker results. Thus, it is comparatively more efficient. | It costs more and generates slower results. Thus, it is comparatively less efficient. |
| Length of Implementation | Implementation is shorter using AI. | The implementation is lengthier using AI. |
| Examples | A few examples include Graph Search and Greedy Search. | A few examples include Breadth-First Search or BFS and Depth-First Search or DFS. |
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