# Artificial Intelligence MCQ (Multiple Choice Questions) - SchoolingAxis

Que- For general graph, how one can get rid of repeated states?

a. By maintaining a list of visited vertices

b. By maintaining a list of traversed edges

c. By maintaining a list of non-visited vertices

d. By maintaining a list of non-traversed edges

Ans- By maintaining a list of visited vertices

Que- DFS is ______ efficient and BFS is __________ efficient.

a. Space, Time

b. Time, Space

c. Time, Time

d. Space, Space

Ans- Space, Time

Que- The main idea of Bidirectional search is to reduce the time complexity by searching two way simultaneously from start node and another from goal node.

a. TRUE

b. False

c. Nothing can be said

d. None of the mentioned

Ans- TRUE

Que- What is the other name of informed search strategy?

a. Simple search

b. Heuristic search

c. Online search

d. None of the mentioned

Ans- Heuristic search

Que- How many types of informed search method are in artificial intelligence?

a. 1

b. 2

c. 3

d. 4

Ans- 4

Que- Which search uses the problem specific knowledge beyond the definition of the problem?

a. Informed search

b. Depth-first search

d. Uninformed search

Ans- Informed search

Que- Which function will select the lowest expansion node at first for evaluation?

a. Greedy best-first search

b. Best-first search

c. Depth-first search

d. None of the mentioned

Ans- Best-first search

Que- What is the heuristic function of greedy best-first search?

a. f(n) != h(n)

b. f(n) < h(n)

c. f(n) = h(n)

d. f(n) > h(n)

Ans- f(n) = h(n)

Que- Which search uses only the linear space for searching?

a. Best-first search

b. Recursive best-first search

c. Depth-first search

d. None of the mentioned

Ans- Recursive best-first search

Que- Which method is used to search better by learning?

a. Best-first search

b. Depth-first search

c. Metalevel state space

d. None of the mentioned

Ans- Metalevel state space

Que- Which search is complete and optimal when h(n) is consistent?

a. Best-first search

b. Depth-first search

c. Both Best-first & Depth-first search

d. A* search

Ans- A* search

Que- Which is used to improve the performance of heuristic search?

a. Quality of nodes

b. Quality of heuristic function

c. Simple form of nodes

d. None of the mentioned

Ans- Quality of heuristic function

Que- Which search method will expand the node that is closest to the goal?

a. Best-first search

b. Greedy best-first search

c. A* search

d. None of the mentioned

Ans- Greedy best-first search

Que- A heuristic is a way of trying

a. To discover something or an idea embedded in a program

b. To search and measure how far a node in a search tree seems to be from a goal

c. To compare two nodes in a search tree to see if one is better than another

d. All of the mentioned

Ans- All of the mentioned

Que- A* algorithm is based on

b. Depth-First -Search

c. Best-First-Search

d. Hill climbing

Ans- Best-First-Search

Que- The search strategy the uses a problem specific knowledge is known as

a. Informed Search

b. Best First Search

c. Heuristic Search

d. All of the mentioned

Ans- All of the mentioned

Que- Uninformed search strategies are better than informed search strategies.

a. TRUE

b. False

c. Nothing can be said

d. None of the mentioned

Ans- TRUE

Que- Best-First search is a type of informed search, which uses ________________ to choose the best next node for expansion.

a. Evaluation function returning lowest evaluation

b. Evaluation function returning highest evaluation

c. Evaluation function returning lowest & highest evaluation

d. None of them is applicable

Ans- Evaluation function returning lowest evaluation

Que- Best-First search can be implemented using the following data structure.

a. Queue

b. Stack

c. Priority Queue

d. Circular Queue

Ans- Priority Queue

Que- The name "best-first search" is a venerable but inaccurate one. After all, if we could really expand the best node first, it would not be a search at all; it would be a straight march to the goal. All we can do is choose the node that appears to be best according to the evaluation function. State whether true or false.

a. TRUE

b. False

c. Nothing can be said

d. None of the mentioned

Ans- TRUE

Que- Heuristic function h(n) is ____

a. Lowest path cost

b. Cheapest path from root to goal node

c. Estimated cost of cheapest path from root to goal node

d. Average path cost

Ans- Estimated cost of cheapest path from root to goal node

Que- Greedy search strategy chooses the node for expansion

a. Shallowest

b. Deepest

c. The one closest to the goal node

d. Minimum heuristic cost

Ans- The one closest to the goal node

Que- In greedy approach evaluation function is

a. Heuristic function

b. Path cost from start node to current node

c. Path cost from start node to current node + Heuristic cost

d. Average of Path cost from start node to current node and Heuristic cost

Ans- Heuristic function

Que- What is the space complexity of Greedy search?

a. O(b)

b. O(bl)

c. O(m)

d. O(bm)

Ans- O(bm)

Que- In A* approach evaluation function is

a. Heuristic function

b. Path cost from start node to current node

c. Path cost from start node to current node + Heuristic cost

d. Average of Path cost from start node to current node and Heuristic cost

Ans- Path cost from start node to current node + Heuristic cost

Que- A* is optimal if h(n) is an admissible heuristic-that is, provided that h(n) never underestimates the cost to reach the goal.

a. TRUE

b. False

c. Nothing can be said

d. None of the mentioned

Ans- TRUE

Que- In many problems the path to goal is irrelevant, this class of problems can be solved using,

a. Informed Search Techniques

b. Uninformed Search Techniques

c. Local Search Techniques

d. Informed & Uninformed Search Techniques

Ans- Local Search Techniques

Que- Though local search algorithms are not systematic, key advantages would include

a. Less memory

b. More time

c. Finds a solution in large infinite space

d. Less memory & Finds a solution in large infinite space

Ans- Less memory & Finds a solution in large infinite space

Que- A complete, local search algorithm always finds goal if one exists, an optimal algorithm always finds a global minimum/maximum. State whether True or False.

a. TRUE

b. False

c. Nothing can be said

d. None of the mentioned

Ans- TRUE

Que- _______________ Is an algorithm, a loop that continually moves in the direction of increasing value - that is uphill

a. Up-Hill Search

b. Hill-Climbing

c. Hill algorithm

d. Reverse-Down-Hill search

Ans- Hill-Climbing

Que- Hill-Climbing algorithm terminates when,

a. Stopping criterion met

b. Global Min/Max is achieved

c. No neighbor has higher value

d. All of the mentioned

Ans- No neighbor has higher value

Que- One of the main cons of hill-climbing search is,

a. Terminates at local optimum & Does not find optimum solution

b. Terminates at global optimum & Does not find optimum solution

c. Does not find optimum solution & Fail to find a solution

d. Fail to find a solution

Ans- Terminates at local optimum & Does not find optimum solution

Que- Stochastic hill climbing chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphil1 move.

a. TRUE

b. False

c. Nothing can be said

d. None of the mentioned

Ans- TRUE

Que- Hill climbing sometimes called ____________ because it grabs a good neighbor state without thinking ahead about where to go next.

a. Needy local search

b. Heuristic local search

c. Greedy local search

d. Optimal local search

Ans- Greedy local search

Que- Hill-Climbing approach stuck for the following reasons

a. Local maxima

b. Ridges

c. Plateaux

d. All of the mentioned

Ans- All of the mentioned

Que- ___________ algorithm keeps track of k states rather than just one.

a. Hill-Climbing search

b. Local Beam search

c. Stochastic hill-climbing search

d. Random restart hill-climbing search

Ans- Local Beam search

Que- A genetic algorithm (or GA) is a variant of stochastic beam search in which successor states are generated by combining two parent states, rather than by modifying a single state.

a. TRUE

b. False

c. Nothing can be said

d. None of the mentioned

Ans- TRUE

Que- Mark two main features of Genetic Algorithm

a. Fitness function & Crossover techniques

b. Crossover techniques & Random mutation

c. Individuals among the population & Random mutation

d. Random mutation & Fitness function

Ans- Fitness function & Crossover techniques

Que- Searching using query on Internet is, use of ___________ type of agent

a. Offline agent

b. Online agent

c. Both Offline & Online agent

d. Goal Based & Online agent

Ans- Goal Based & Online agent

Que- _________________ are mathematical problems defined as a set of objects whose state must satisfy a number of constraints or limitations.

a. Constraints Satisfaction Problems

b. Uninformed Search Problems

c. Local Search Problems

d. All of the mentioned

Ans- Constraints Satisfaction Problems

Que- Which of the Following problems can be modeled as CSP?

a. 8-Puzzle problem

b. 8-Queen problem

c. Map coloring problem

d. All of the mentioned

Ans- All of the mentioned

Que- What among the following constitutes to the incremental formulation of CSP?

a. Path cost

b. Goal cost

c. Successor function

d. All of the mentioned

Ans- All of the mentioned

Que- The term ___________ is used for a depth-first search that chooses values for one variable at a time and returns when a variable has no legal values left to assign.

a. Forward search

b. Backtrack search

c. Hill algorithm

d. Reverse-Down-Hill search

Ans- Backtrack search

Que- To overcome the need to backtrack in constraint satisfaction problem can be eliminated by

a. Forward Searching

b. Constraint Propagation

c. Backtrack after a forward search

d. Omitting the constraints and focusing only on goals

Ans- Forward Searching

Que- The BACKTRACKING-SEARCH algorithm in Figure 5.3 has a very simple policy for what to do when a branch of the search fails: back up to the preceding variable and try a different value for it. This is called chronological-backtracking. It is also possible to go all the way to set of variable that caused failure. State whether True or False.

a. TRUE

b. False

c. Nothing can be said

d. None of the mentioned

Ans- TRUE

Que- Consider a problem of preparing a schedule for a class of student. This problem is a type of

a. Search Problem

b. Backtrack Problem

c. CSP

d. Planning Problem

Ans- CSP

Que- Constraint satisfaction problems on finite domains are typically solved using a form of ___________

a. Search Algorithms

b. Heuristic Search Algorithms

c. Greedy Search Algorithms

d. All of the mentioned

Ans- All of the mentioned

Que- Solving a constraint satisfaction problem on a finite domain is an/a ___________ problem with respect to the domain size.

a. P complete

b. NP complete

c. NP hard

d. Domain dependent

Ans- NP complete

Que-  ____________ is/are useful when the original formulation of a problem is altered in some way, typically because the set of constraints to consider evolves because of the environment.

a. Static CSPs

b. Dynamic CSPs

c. Flexible CSPs

d. None of the mentioned

Ans- Dynamic CSPs

Que- Flexible CSPs relax on _______

a. Constraints

b. Current State

c. Initial State

d. Goal State

Ans- Constraints

Que- Language/Languages used for programming Constraint Programming includes

a. Prolog

b. C Sharp

c. C

d. Fortrun

Ans- Prolog

Que- Backtracking is based on,

a. Last in first out

b. First in first out

c. Recursion

d. Both Last in first out & Recursion

Ans- Both Last in first out & Recursion

Que- Constraint Propagation technique actually modifies the CSP problem.

a. TRUE

b. False

c. Nothing can be said

d. None of the mentioned

Ans- TRUE

Que- When do we call the states are safely explored?

a. A goal state is unreachable from any state

b. A goal state is denied access

c. A goal state is reachable from every state

d. None of the mentioned

Ans- A goal state is reachable from every state

Que- Which of the following algorithm is generally used CSP search algorithm?

b. Depth-first search algorithm

c. Hill-climbing search algorithm

d. None of the mentioned

Ans- Depth-first search algorithm

Que- General games involves

a. Single-agent

b. Multi-agent

c. Neither Single-agent nor Multi-agent

d. Only Single-agent and Multi-agent

Ans- Only Single-agent and Multi-agent

a. Competitive Environment

b. Cooperative Environment

c. Neither Competitive nor Cooperative Environment

d. Only Competitive and Cooperative Environment

Ans- Competitive Environment

Que- Mathematical game theory, a branch of economics, views any multi-agent environment as a game provided that the impact of each agent on the others is "significant," regardless of whether the agents are cooperative or competitive.

a. TRUE

b. False

c. Nothing can be said

d. None of the mentioned

Ans- TRUE

Que- Zero sum games are the one in which there are two agents whose actions must alternate and in which the utility values at the end of the game are always the same.

a. TRUE

b. False

c. Nothing can be said

d. None of the mentioned

Ans- False

Que- Zero sum game has to be a ______ game.

a. Single player

b. Two player

c. Multiplayer

d. Three player

Ans- Multiplayer

Que- A game can be formally defined as a kind of search problem with the following components:

a. Initial State

b. Successor Function

c. Terminal Test

d. All of the mentioned

Ans- All of the mentioned

Que- The initial state and the legal moves for each side define the __________ for the game.

a. Search Tree

b. Game Tree

c. State Space Search

d. Forest

Ans- Game Tree

Que- General algorithm applied on game tree for making decision of win/lose is ____________

a. DFS/BFS Search Algorithms

b. Heuristic Search Algorithms

c. Greedy Search Algorithms

d. MIN/MAX Algorithms

Ans- MIN/MAX Algorithms