MCQOPTIONS
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MCQOPTIONS
This section includes 103 Mcqs, each offering curated multiple-choice questions to sharpen your Computer Science Engineering (CSE) knowledge and support exam preparation. Choose a topic below to get started.
| 1. |
• is evaluated last. |
| A. | true |
| B. | false |
| Answer» B. false | |
| 2. |
Is the below statement true for the domain of positive integers ∀p ∃q ( p + q = 7) |
| A. | yes |
| B. | no |
| Answer» B. no | |
| 3. |
__________ 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 |
| Answer» C. hill algorithm | |
| 4. |
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 |
| Answer» D. none of the above | |
| 5. |
Is ∀z S(x,y) a well-formed formula? |
| A. | yes |
| B. | no |
| Answer» B. no | |
| 6. |
What is the nature of the sentence: ∀x ∀y (TeacherOf(x,y) ⊃ StudentOf(y,x)) |
| A. | symmetry |
| B. | exhaustiveness |
| C. | inverse |
| D. | none of the above |
| Answer» D. none of the above | |
| 7. |
∀x ∃ y P(x,y) ≡ ∃ y ∀ x P(x,y) |
| A. | yes |
| B. | no |
| Answer» C. | |
| 8. |
What is used to lable primitive problems in AO problem? |
| A. | unvisited |
| B. | unsolved |
| C. | solved |
| D. | visited |
| Answer» D. visited | |
| 9. |
What is the heuristic cost of SOLVED nodes in AO* example? |
| A. | 0 |
| B. | 1 |
| C. | 2 |
| D. | 3 |
| Answer» B. 1 | |
| 10. |
What cost is assumed for arc while solving AO* progress example? |
| A. | 0 |
| B. | 1 |
| C. | 2 |
| D. | 3 |
| Answer» C. 2 | |
| 11. |
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 |
| Answer» D. none of the above | |
| 12. |
What arcs represents in AO Graph? |
| A. | subproblem to be solved individually |
| B. | solution |
| C. | path |
| D. | sequence of actions |
| Answer» B. solution | |
| 13. |
Which are the first AI applications of AO graph? |
| A. | saint |
| B. | xcon |
| C. | dendral |
| D. | both a and c |
| Answer» E. | |
| 14. |
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 |
| Answer» E. | |
| 15. |
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 |
| Answer» B. the predecessor state s when action a is applied to state s. | |
| 16. |
effects⺠(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 |
| Answer» D. none of the above | |
| 17. |
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 |
| Answer» E. | |
| 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 |
| Answer» B. the goal state over action a when applied to goal g. | |
| 19. |
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 |
| Answer» C. leads to huge search space | |
| 20. |
What is the main advantage of backward state-space search? |
| A. | cost |
| B. | actions |
| C. | relevant actions |
| D. | all of the mentioned |
| Answer» D. all of the mentioned | |
| 21. |
What is true aboout 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 |
| Answer» D. is a depth-first search with a fixed depth limit l | |
| 22. |
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 |
| Answer» C. contigent link | |
| 23. |
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 |
| Answer» B. triple (s1, s2, o) | |
| 24. |
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 |
| Answer» C. unary constraint | |
| 25. |
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 |
| Answer» D. none of the above | |
| 26. |
Production/Rule looks like________________ |
| A. | pattern-->data |
| B. | action-->data |
| C. | pattern-->action |
| D. | none of the above |
| Answer» D. none of the above | |
| 27. |
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 |
| Answer» C. to unrecord forbidden moves, which are referred to as tabu moves . | |
| 28. |
Which is not problem in Hill climing? |
| A. | plateau |
| B. | ridges |
| C. | local maximum |
| D. | landscape |
| Answer» E. | |
| 29. |
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 |
| Answer» C. terminates when the goal node is not found. | |
| 30. |
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 |
| Answer» E. | |
| 31. |
_______________________requires Linear Space but uses backtracking |
| A. | breadth first search |
| B. | recursive best first search (rbfs) |
| C. | a* |
| D. | ida* |
| Answer» C. a* | |
| 32. |
What is the termination criteria in Hill climbing? |
| A. | when no successor of the node has better heuristic value. |
| B. | when successor of the node has better heuristic value. |
| C. | when no ancestor of the node has better heuristic value. |
| D. | when ancestor of the node has better heuristic value. |
| Answer» B. when successor of the node has better heuristic value. | |
| 33. |
Which property asks that the algorithm is locally admissible? |
| A. | admissibility |
| B. | monotonicity |
| C. | informedness |
| D. | none of the above |
| Answer» C. informedness | |
| 34. |
Admissibility of the heuristic function is given as: |
| A. | h(n)>= h*(n) |
| B. | h(n)< h*(n) |
| C. | h(n)== h*(n) |
| D. | h(n)<= h*(n) |
| Answer» E. | |
| 35. |
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 |
| Answer» D. best first search | |
| 36. |
The efficiency of A* algorithm depends on __________________________ |
| A. | depth |
| B. | the quality of heuristic |
| C. | unknown nodes |
| D. | d. none of the above |
| Answer» C. unknown nodes | |
| 37. |
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 |
| Answer» C. not able to find | |
| 38. |
Which of the following are Informed search algorithms? |
| A. | best first search |
| B. | a* search |
| C. | iterative deeping search |
| D. | both a & b |
| Answer» E. | |
| 39. |
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 |
| Answer» E. | |
| 40. |
The components of Expert system are? |
| A. | a set of rules, the inference engine (ie), forward chaining |
| B. | a set of rules, backward chaining, a working memory (wm) |
| C. | a set of rules, the inference engine (ie), a working memory (wm) |
| D. | a set of rules, forward chaining, backward chaining |
| Answer» D. a set of rules, forward chaining, backward chaining | |
| 41. |
In which chaining, the Left-Hand side is used to match the rules and Right-Hand side is used to check the effect of using the rule. |
| A. | forward chaining |
| B. | backward chaining |
| C. | reverse chaining |
| D. | both b & c |
| Answer» B. backward chaining | |
| 42. |
The start node for search in plan space planning is_______________ |
| A. | bfs |
| B. | dfs |
| C. | both dfs and bfs |
| D. | a* |
| Answer» D. a* | |
| 43. |
Agent interacts with the world via _______________ and ______________ |
| A. | decision , effect |
| B. | perception, decision |
| C. | perception, action |
| D. | perception, effect |
| Answer» D. perception, effect | |
| 44. |
What is true about Linear Planning? |
| A. | it refers to the fact that the subgoals are attempted and solved in a linear order. |
| B. | attempts to solve subgoals individually one after another. |
| C. | attempts to solve subgoal individually in non linear fashion |
| D. | both a & b |
| Answer» E. | |
| 45. |
Goal Stack Planning breaks up a ______________________________ |
| A. | initial state |
| B. | stack in different part |
| C. | set of goal predicates into individual subgoals |
| D. | all of the above |
| Answer» D. all of the above | |
| 46. |
what is the issue of Forward State Space Planning? |
| A. | low banching factor. |
| B. | large branching factor. |
| C. | work in forward fashion |
| D. | work in backward fashion |
| Answer» C. work in forward fashion | |
| 47. |
iv. Compound Logic" |
| A. | i. and ii. |
| B. | i. and iii. |
| C. | ii. and iii. |
| D. | iii. and iv. |
| Answer» D. iii. and iv. | |
| 48. |
The statement comprising the limitations of FOL is/are ____________ |
| A. | expressiveness |
| B. | formalizing natural languages |
| C. | many-sorted logic |
| D. | all of the mentioned |
| Answer» E. | |
| 49. |
∀x ∃ y P(x,y) ≡ ∃ y ∀ x P(x,y) |
| A. | yes |
| B. | no |
| Answer» C. | |
| 50. |
Which of the following is a sound rule of inference? |
| A. | q ∧ (p → q) → p |
| B. | p → (p ∨ q) |
| C. | q ∨ (p → q) → p |
| D. | all of above |
| Answer» C. q ∨ (p → q) → p | |