In iterative deepening DFS algorithms, the search process of level 1 and 2 takes place. It continues the exploration until it finds the solution. It generates nodes until it finds the goal node and saves the stack of nodes it had created.
For an Artificial Intelligence interview question about iterative deepening depth-first search (IDDFS) algorithms, you could provide the following answer:
Iterative deepening depth-first search (IDDFS) is a combination of depth-first search (DFS) and breadth-first search (BFS) algorithms. It is used to search for a target node in a graph or tree data structure.
In IDDFS, the search begins with a depth limit of 0, performing a DFS up to that depth. If the target node is not found, the depth limit is increased by 1, and the search is performed again. This process continues until the target node is found or until the entire tree or graph has been searched.
The main advantage of IDDFS is its ability to combine the space efficiency of DFS with the completeness of BFS. It guarantees to find the shortest path from the initial node to the target node in a finite graph or tree with non-negative edge weights.
IDDFS is particularly useful in scenarios where the search space is large and memory usage is a concern, as it only requires storing information for nodes within the current depth limit. Additionally, it can be more efficient than BFS in terms of memory usage because it only maintains the state of nodes at each depth level rather than storing all nodes at once. However, IDDFS may not be as efficient as other search algorithms such as A* search when heuristics are available.