- Why is DFS incomplete?
- How does unified search is optimal?
- Why depth first search is not optimal?
- Will iterative deepening search always be optimal?
- Is BFS complete and optimal?
- Is iterative deepening faster than BFS?
- Is optimality and completeness exist in bidirectional search algorithm?
- Under what condition is breadth first search optimal?
- IS A * search optimal?
- What is advantage of A * graph search over A * tree search?
- Is breadth first search optimal why?
- Is the algorithm guaranteed to find a solution when there is one?

## Why is DFS incomplete?

1 Answer.

Depth-first tree search can get stuck in an infinite loop, which is why it is not “complete”.

Graph search keeps track of the nodes it has already searched, so it can avoid following infinite loops.

“Redundant paths” are different paths which lead from the same start node to the same end node..

## How does unified search is optimal?

Uniform-cost search is always optimal as it only selects a path with the lowest path cost.

## Why depth first search is not optimal?

Optimal as in “produces the optimal path”, not “is the fastest algorithm possible”. When searching a state space for a path to a goal, DFS may produce a much longer path than BFS. Note that BFS is only optimal when actions are unweighted; if different actions have different weights, you need something like A*.

## Will iterative deepening search always be optimal?

DFS is not guaranteed to find an optimal path; iterative deepening is. DFS may explore the entire graph before finding the target node; iterative deepening only does this if the distance between the start and end node is the maximum in the graph.

## Is BFS complete and optimal?

BFS is complete — if the shallowest goal node is at depth d, it will eventually find it after expanding all the nodes shallower than d. … BFS is optimal if the path cost is a non-decreasing function of d. Usually, BFS is applied when all the actions have the same cost.

## Is iterative deepening faster than BFS?

Consider a search tree with the same branching factor at each level; most of the nodes will be on the bottom level so it does not matter much to generate upper level nodes repeatedly. The result is that Iterative Deepening is faster than BFS although Frank says that it is slower but it uses alot less memory than BFS.

## Is optimality and completeness exist in bidirectional search algorithm?

Is optimality and completeness exist in bidirectional search algorithm? Explanation: Yes, optimality and completeness both exist in bidirectional search algorithm.

## Under what condition is breadth first search optimal?

BFS is optimal if the path cost is a non-decreasing function of d(depth). Normally, BFS is applied when all the actions have the same cost. Optimal as in “produces the optimal path”, not “is the fastest algorithm possible”.

## IS A * search optimal?

Algorithm A* is a best-first search algorithm that relies on an open list and a closed list to find a path that is both optimal and complete towards the goal.

## What is advantage of A * graph search over A * tree search?

The advantage of graph search obviously is that, if we finish the search of a node, we will never search it again. On the other hand, the tree search can visit the same node multiple times. The disadvantage of graph search is that it uses more memory (which we may or may not have) than tree search.

## Is breadth first search optimal why?

breadth-first search is optimal if the path cost is a nondecreasing function of the depth of the node. The most common such scenario is that all actions have the same cost. … Therefore I think for BFS to be optimal, cost function should be non decreasing AND the costs of nodes should be identical.

## Is the algorithm guaranteed to find a solution when there is one?

When a search algorithm has the property of completeness, it means that if a solution to a given problem exists, the algorithm is guaranteed to find it. … The two fundamental properties a heuristic function can have are admissibility and consistency.