Identifying Uninformed Search Algorithms

Uninformed search algorithms, also known as blind search, explore the search space systematically without using any specific knowledge about the problem domain or the goal's location. They expand nodes based solely on the information available during the search process.

Common Uninformed Search Algorithms

• Breadth First Search (BFS): Explores neighbours first before moving to the next level neighbours.
• Depth First Search (DFS): Explores as far as possible along each branch before backtracking.
• Depth-limited Search (DLS): A modified DFS that stops exploring a path once a specified depth limit is reached.
• Uniform Cost Search (UCS): Expands nodes with the lowest path cost first.

Understanding Informed Search Algorithms

In contrast, informed search algorithms utilize a heuristic function, $h(n)$, which estimates the cost from the current node $n$ to the nearest goal. This heuristic guides the search more efficiently towards the goal.

Analyzing A* Search

A* Search is a prominent example of an informed search algorithm. It evaluates nodes using a function $f(n) = g(n) + h(n)$, where $g(n)$ is the known cost from the start node to node $n$, and $h(n)$ is the heuristic estimate of the cost from node $n$ to the goal. Because A* uses the heuristic function $h(n)$, it is considered an informed search strategy.

Conclusion

Since A* Search employs heuristic information to guide its search, it does not belong to the category of uninformed search algorithms. The other options (BFS, DFS, DLS) are standard uninformed search techniques.