Introduction

Loading, please wait ...

Open its course - Artificial Intelligence

Introduction

To be...
View Complete Description

Topic Questions: 60
MCQs	T/F	FiB	SQ
45	0	0	15

Question Type Legends
	Easy		Medium		Difficult

MCQs T/F FiB Short Qs

Open its course - Artificial Intelligence

Multiple Choice Questions for Introduction

×
Hidden Questions!

Some MCQs are not visible. Usually teachers hides their visibility if they are few in number. We recommends them to have more than 70+ questions against the topic and make them public.
1 Explain the concept of a consistent heuristic function in the context of A* search.
1. A heuristic function that never overestimates the actual cost to the goal.
2. A heuristic function that is always non-negative.
3. A heuristic function that is admissible but not monotonic.
4. A heuristic function that is used in conjunction with a closed list.
Explaination:
A consistent heuristic function satisfies the triangle inequality, ensuring that the estimated cost to the goal never decreases as the search progresses.
- View Answer
2 What is the main drawback of using a greedy best-first search algorithm?
1. It may not always find the optimal solution.
2. It can be computationally expensive.
3. It requires a large amount of memory.
4. It is only applicable to binary trees.
Explaination:
Greedy best-first search selects the node with the best heuristic value at each step, which may lead to suboptimal solutions if the heuristic is not accurate.
- View Answer
3 Which search algorithm is particularly well-suited for game playing, where the search space is often very large?
1. Depth-First Search
2. Breadth-First Search
3. A* Search
4. Minimax
Explaination:
Minimax is a depth-first search algorithm that explores possible moves in a game tree, evaluating the potential outcomes to determine the best move for the current player.
- View Answer
4 What is the purpose of a heuristic function in informed search algorithms?
1. To estimate the cost of reaching the goal from a given node.
2. To determine the order in which nodes are explored.
3. To prevent the search from getting stuck in local optima.
4. To ensure that the search terminates.
Explaination:
Heuristic functions provide an estimate of the remaining cost to reach the goal, guiding the search towards promising paths.
- View Answer
5 What is the primary difference between a tree search and a graph search?
1. Tree search explores nodes repeatedly, while graph search avoids revisiting explored nodes.
2. Tree search is always faster than graph search.
3. Tree search uses heuristics, while graph search does not.
4. Tree search is only applicable to binary trees.
Explaination:
Graph search maintains a set of explored nodes to prevent redundant exploration, while tree search may explore the same node multiple times.
- View Answer
6 Which search algorithm is most suitable for finding the shortest path in a graph with a large number of nodes?
1. Depth-First Search
2. Breadth-First Search
3. A* Search
4. Uniform Cost Search
Explaination:
A* Search is an informed search algorithm that uses a heuristic function to guide the search towards the goal, making it more efficient than uninformed search algorithms like DFS and BFS in large graphs.
- View Answer
7 What is the main drawback of the Breadth-First Search algorithm?
1. Excessive memory usage
2. Inability to find the shortest path
3. Getting stuck in loops
4. Slow execution in unweighted graphs
Explaination:
Breadth-First Search can consume a large amount of memory, as it needs to store all the nodes at the current depth level before moving to the next level.
- View Answer
8 Which of the following best describes the 'open list' in state space search?
1. A list of all explored states
2. A list of states to be explored
3. A list of successor states
4. A list of goal states
Explaination:
The 'open list' contains nodes (states) that have been discovered but not yet explored in the search process.
- View Answer
9 What is the primary goal of the Minimax algorithm?
1. To minimize the maximum gain of the opponent
2. To maximize the minimum gain of the player
3. To find the shortest path
4. To minimize the search space
Explaination:
The Minimax algorithm aims to maximize the minimum gain of the player, assuming that the opponent is also playing optimally to minimize the player's gain.
- View Answer
10 What is the key difference between informed and uninformed search strategies?
1. Informed search strategies use heuristics to guide the search process
2. Uninformed search strategies use heuristics to guide the search process
3. Informed search strategies do not require a goal state
4. Uninformed search strategies guarantee finding the shortest path
Explaination:
Informed search strategies, such as A* and Greedy Best-First Search, use heuristic functions to estimate the cost to reach the goal and guide the search.
- View Answer
11 Which of the following is true about A* search algorithm?
1. A* search algorithm is not optimal
2. A* search algorithm is only applicable to unweighted graphs
3. A* search algorithm is optimal if the heuristic function is admissible and consistent
4. A* search algorithm does not use heuristics
Explaination:
A* search algorithm is guaranteed to find the optimal solution if the heuristic is both admissible and consistent.
- View Answer
12 What is a heuristic function?
1. A function that calculates the exact cost to reach the goal
2. A function that estimates the cost of the cheapest path from a given state to the goal state
3. A function that guarantees finding the optimal solution
4. A function that only works for deterministic algorithms
Explaination:
A heuristic function estimates the cost of the cheapest path from a given state to the goal state, guiding informed search algorithms.
- View Answer
13 In state space search, what does a node represent?
1. A possible solution
2. A state
3. An operator
4. A heuristic function
Explaination:
In state space search, a node represents a state in the search space.
- View Answer

Programs

Introduction

Multiple Choice Questions for Introduction

Hidden Questions!

Subject Expert

Learning Experience Platform