Problem-solving in Artificial Intelligence (AI) involves finding solutions to complex problems using various search algorithms. These algorithms are typically categorized as uninformed, informed, or local search methods, each having its unique approach, advantages, and limitations. The primary goal is to find the most efficient path from an initial state to a goal state, often while optimizing factors like cost, time, and memory usage.
| Uninformed Search | Informed Search |
|---|---|
| It doesn’t use knowledge for searching process. | It uses knowledge for the searching process. |
| It finds solution slow as compared to informed search. | It finds solution more quickly. |
| It is always complete. | It may or may not be complete. |
| Cost is high. | Cost is low. |
| It consumes moderate time. | It consumes less time. |
| No suggestion is given regarding the solution in it. | It provides the direction regarding the solution. |
| It is more lengthy while implementation. | It is less lengthy while implementation. |
| BFS,DFS,DLS,UCS | Greedy Search, A* Search, Graph Search |
Uninformed search algorithms, also known as blind search algorithms, do not use any domain-specific knowledge beyond the problem's definition. These algorithms explore the search space systematically, without any additional information to guide the search.
Overview:
BFS explores the search space level by level, starting from the root node and moving outward. It uses a First-In-First-Out (FIFO) queue to manage the nodes that need to be explored. BFS is guaranteed to find the shortest path to the goal in an unweighted graph.
Key Concepts:
Example: