Depth-limited search

Depth-limited search is a variation of depth-first search that sets a maximum depth limit for the search. It is often used in situations where the depth of the solution is known or where the search space is too large to explore completely.

In depth-limited search, the search process is the same as in DFS, except that a maximum depth limit is imposed on the search. If the limit is reached and the goal state has not been found, the search is terminated, and the algorithm backtracks to the previous node to continue the search from there.

Depth-limited search has some advantages over DFS. It avoids the problem of getting stuck in infinite loops that can occur in DFS when the graph contains cycles. It is also more memory-efficient than BFS since it only keeps track of nodes at a certain depth. However, depth-limited search may still miss the solution if the limit is set too low, and it may also fail to find the optimal solution.

One way to address the limitations of depth-limited search is to use iterative deepening depth-first search (IDDFS), which repeatedly performs depth-limited searches with increasing depth limits until the goal state is found. IDDFS is complete and optimal as long as the cost of each action is non-negative.

Depth-limited search is commonly used in applications such as game-playing, where the search space is too large to explore completely, but the depth of the solution is known. It is also used in artificial intelligence applications such as natural language processing and planning.