Most asked Artificial Intelligence mcqs

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Artificial intelligence and robotics mcq SPPU questions and answers

1. What is true about Artificial Intelligence

A. The ability to solve problems.
B. The ability to act rationally.
C. The ability to act like humans
D. All of the above

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All of the above

2. Which of the following are Informed search algorithms

A. Best First Search
B. A* Search
C. Iterative Deeping Search
D. Both a & b

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Both a & b

3. If there is a solution, breadth first search is _______________to find it

A. Difficult
B. Guaranteed
C. Not able to find
D. None of the above

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Guaranteed

4. Which search strategy is combining the benefits of both BFS and DFS?

A. Depth Limited Search
B. A*
C. Iterative Deepening Depth first search
D. Best first search

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Iterative Deepening Depth first search

5. What is true about variable neighborhood function?

A. Neighbourhood functions that are sparse lead to quicker movement during search
B. algorithm has to inspect very fewer neighbours
C. VDN stars searching with sparse Neighbourhood functions, when it reaches an optimum, it
switches to denser function.
D. All of the above

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All of the above

6. _______________________requires Linear Space but uses backtracking

A. Breadth First Search
B. Recursive Best First Search (RBFS)
C. A*
D. IDA*

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Recursive Best First Search (RBFS)

7. Which property asks that the algorithm is locally admissible?

A. Admissibility
B. Monotonicity
C. Informedness
D. None of the above

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Monotonicity

8. A* Search Algorithm __

A. does not expand the node which have the lowest value of f(n),
B. finds the shortest path through the search space using the heuristic function i.e f(n)=g(n) + h(n)
C. terminates when the goal node is not found.
D. All of the above

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finds the shortest path through the search space using the heuristic function i.e f(n)=g(n) + h(n)

9. Which is not problem in Hill climbing?

A. Plateau
B. Ridges
C. Local Maximum
D. landscape

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landscape

10. Tabu search is designed __

A. as it does not follow aspiration criteria
B. to escape the trap of local optimality.
C. to unrecord forbidden moves, which are referred to as tabu moves .
D. All of the above

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to escape the trap of local optimality.

AIR mcq sppu

11. How can we convert AO graph with mixed nodes into graph with pure AND and OR nodes?

A. By traversing multiple node
B. By deleting one of the node
C. By addition of extra node
D. None of the above

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By addition of extra node

12. Arc consistency in AO graph is concernd with _

A. Nodes
B. finding consistent values for pairs of variables.
C. unary constraint
D. All of the above

finding consistent values for pairs of variables.

13. A planning problem P in BSSP is defined as a _

A. triple (S, G, O)
B. triple (S1, S2, O)
C. triple (G1, G, O)
D. None of the above

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triple (S, G, O)

14. Plan representation in Plan Space Planning is done with__ ———–links

A. binding links
B. ordering links and casual link
C. Contigent link
D. head step

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ordering links and casual link

15. What is true about Iterative Deepening DFS?

A. It does not perform DFS in a BFS fashion.
B. It is the preferred informed search method
C. It’s a Depth First Search, but it does it one level at a time, gradually increasing the limit, until a goal is found.
D. Is a depth-first search with a fixed depth limit l

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It’s a Depth First Search, but it does it one level at a time, gradually increasing the limit, until a goal is found.

16. What is the main advantage of backward state-space search?

A. Cost
B. Actions
C. Relevant actions
D. All of the mentioned

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Relevant actions

17. Backward State Space Planning (BSSP)_

A. simply explores the set of all future states in possible order
B. Start searching backwards from the goal
C. leads to huge search space
D. has no sense of direction

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Start searching backwards from the goal

18. In Backward State Space Planning ,regress(A,G) that returns _

A. the regressed goal over action A when applied to goal G.
B. the goal state over action A when applied to goal G.
C. the initial state over action A when applied to goal G.
D. Both A & B

the regressed goal over action A when applied to goal G.

19. What is true about Backward State Space Planning?

A. goal states are often incompletely specified.
B. expresses only what is desired in the final state, rather than a complete description of the final state.
C. It uses regression
D. All of the above

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All of the above

Artificial Intelligence and Robotics mcqs questions

20. effects⁺ (a) in Forward State Space Planning denotes __

A. denotes the set of negative effects of action a
B. denotes the set of neutral effects of action a
C. denotes the set of positive effects of action a
D. None of the above

denotes the set of positive effects of action a

21. In Forward State Space Planning , Progress ( A, S) function returns _

A. the successor state S when action A is applied to state S.
B. the predecessor state S when action A is applied to state S.
C. Both A & B
D. None of the above

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the successor state S when action A is applied to state S.

22. What are the drawbacks of Forward State Space Planning?

A. FSSP has very huge search space
B. It includes the actions that have nothing go do with achieving the goal
C. Regression is used in Forward State Space Planning
D. Both A & B

Both A & B

23. What arcs represents in AO Graph?

A. subproblem to be solved individually
B. solution
C. Path
D. Sequence of actions

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subproblem to be solved individually

24. Which are the first AI applications of AO graph?

A. SAINT
B. XCON
C. DENDRAL
D. Both A and C

Both A and C

25. What is Hyper-Edge in AO Graph?

A. Many edges together can be Hyber edge
B. Those are AND Edges only
C. Both 1 and 2
D. None of the above

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Both 1 and 2

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