Table of Contents

## How do you detect a back edge in DFS traversing?

To detect a back edge, keep track of vertices currently in the recursion stack of function for DFS traversal. If a vertex is reached that is already in the recursion stack, then there is a cycle in the tree. The edge that connects the current vertex to the vertex in the recursion stack is a back edge.

**What are the three possible kinds of non tree edges when performing a DFS on a directed graph?**

The other edges of G can be divided into three categories:

- Back edges point from a node to one of its ancestors in the DFS tree.
- Forward edges point from a node to one of its descendants.
- Cross edges point from a node to a previously visited node that is neither an ancestor nor a descendant.

**How do you do depth first traversal?**

Data Structure – Depth First Traversal

- Rule 1 ˆ’ Visit the adjacent unvisited vertex. Mark it as visited. Display it. Push it in a stack.
- Rule 2 ˆ’ If no adjacent vertex is found, pop up a vertex from the stack. (It will pop up all the vertices from the stack, which do not have adjacent vertices.)
- Rule 3 ˆ’ Repeat Rule 1 and Rule 2 until the stack is empty.

### What can be the applications of depth first search?

Applications. Depth-first search is used in topological sorting, scheduling problems, cycle detection in graphs, and solving puzzles with only one solution, such as a maze or a sudoku puzzle. Other applications involve analyzing networks, for example, testing if a graph is bipartite.

**Which is better BFS or DFS?**

BFS is better when target is closer to Source. DFS is better when target is far from source. As BFS considers all neighbour so it is not suitable for decision tree used in puzzle games. DFS is more suitable for decision tree.

**Why do we use BFS and DFS?**

BFS(Breadth First Search) uses Queue data structure for finding the shortest path. DFS(Depth First Search) uses Stack data structure. 3. BFS can be used to find single source shortest path in an unweighted graph, because in BFS, we reach a vertex with minimum number of edges from a source vertex.

## Why BFS takes more memory than DFS?

For implementation, BFS uses a queue data structure, while DFS uses a stack. BFS uses a larger amount of memory because it expands all children of a vertex and keeps them in memory. It stores the pointers to a level’s child nodes while searching each level to remember where it should go when it reaches a leaf node.

**Why stack is used in DFS?**

BFS uses always queue, Dfs uses Stack data structure. As the earlier explanation tell about DFS is using backtracking. Remember backtracking can proceed only by Stack. The depth-first search uses a Stack to remember where it should go when it reaches a dead end.

**When DFS of a graph is unique?**

When the Depth First Search of a graph is unique? Explanation: When Every node will have one successor then the Depth First Search is unique. In all other cases, when it will have more than one successor, it can choose any of them in arbitrary order.

### Can DFS be used to find shortest path?

And so, the only possible way for BFS (or DFS) to find the shortest path in a weighted graph is to search the entire graph and keep recording the minimum distance from source to the destination vertex.

**Does DFS find shortest path?**

There are several differences between DFS and BFS (short answer: Both of them can find the shortest path in the unweighted graph). Both BFS and DFS will give the shortest path from A to B if you implemented right.

**Is Dijkstra BFS or DFS?**

According to this page, Dijkstra’s algorithm is just BFS with a priority queue.

## Which algorithm is best for Shortest Path?

What Is the Best Shortest Path Algorithm?

- Dijkstra’s Algorithm. Dijkstra’s Algorithm stands out from the rest due to its ability to find the shortest path from one node to every other node within the same graph data structure.
- Bellman-Ford Algorithm.
- Floyd-Warshall Algorithm.
- Johnson’s Algorithm.
- Final Note.

**Why DFS is not optimal?**

Completeness: DFS is complete if the search tree is finite, meaning for a given finite search tree, DFS will come up with a solution if it exists. Optimality: DFS is not optimal, meaning the number of steps in reaching the solution, or the cost spent in reaching it is high.

**Is it mandatory that a complete algorithm is optimal as well?**

Yes, by definition. Finding the optimal solution entails proving optimality. This can be done by finding all solutions or by proving that no solution can have a better cost than the one found already. In both cases, at least one solution has to be found.

### Is a * optimal?

Since A* only can have as a solution a node that it has selected for expansion, it is optimal.

**WHAT IS A * search in AI?**

A* (pronounced “A-star”) is a graph traversal and path search algorithm, which is often used in many fields of computer science due to its completeness, optimality, and optimal efficiency. One major practical drawback is its. space complexity, as it stores all generated nodes in memory.

**Is Google an AI?**

Google AI is a division of Google dedicated to artificial intelligence….Google AI.

Industry | Artificial intelligence |
---|---|

Founded | 2017 |

Owner | |

Website | www.ai.google |

## How overestimation is handled in A * algorithm?

The algorithm continues until a goal node has a lower f value than any node in the queue (or until the queue is empty). With overestimation, A* has no idea when it can stop exploring a potential path as there can be paths with lower actual cost but higher estimated cost than the best currently known path to the goal.

**What is AO * algorithm?**

AO* Algorithm basically based on problem decompositon (Breakdown problem into small pieces) When a problem can be divided into a set of sub problems, where each sub problem can be solved separately and a combination of these will be a solution, AND-OR graphs or AND – OR trees are used for representing the solution.

**What is difference between A * and AO * algorithm?**

An A* algorithm represents an OR graph algorithm that is used to find a single solution (either this or that). An AO* algorithm represents an AND-OR graph algorithm that is used to find more than one solution by ANDing more than one branch.

### How does Minimax algorithm work?

The Minimax algorithm helps find the best move, by working backwards from the end of the game. At each step it assumes that player A is trying to maximize the chances of A winning, while on the next turn player B is trying to minimize the chances of A winning (i.e., to maximize B’s own chances of winning).

**What is best first search algorithm in AI?**

The Greedy BFS algorithm selects the path which appears to be the best, it can be known as the combination of depth-first search and breadth-first search. Greedy BFS makes use of Heuristic function and search and allows us to take advantages of both algorithms.

**Is Dijkstra always optimal?**

Dijkstra’s algorithm is used for graph searches. It is optimal, meaning it will find the single shortest path. It is uninformed, meaning it does not need to know the target node before hand. In fact it finds the shortest path from every node to the node of origin.

## Which algorithm is used to solve any kind of problem?

Tree algorithm

**Which data structure is used for best first search?**

priority queue

**Why is a * better than best first search?**

A* achieves better performance by using heuristics to guide its search. A* combines the advantages of Best-first Search and Uniform Cost Search: ensure to find the optimized path while increasing the algorithm efficiency using heuristics. If h(n)=0, then A* turns to be Uniform-Cost Search.

### What is the heuristic function of A * search?

A heuristic function, also called simply a heuristic, is a function that ranks alternatives in search algorithms at each branching step based on available information to decide which branch to follow. For example, it may approximate the exact solution.

**Which is used to improve the agents performance?**

7. Which is used to improve the agents performance? Explanation: An agent can improve its performance by storing its previous actions.