McqMate
These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Computer Science Engineering (CSE) .
| 51. |
Which of the following is an extension of the semantic network? |
| A. | expert systems |
| B. | rule based expert systems |
| C. | decision tree based networks |
| D. | partitioned networks |
| Answer» D. partitioned networks | |
| 52. |
Is the below statement true for the domain of positive integers ∀p ∃q ( p + q = 7) |
| A. | yes |
| B. | no |
| Answer» A. yes | |
| 53. |
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» B. p → (p ∨ q) | |
| 54. |
∀x ∃ y P(x,y) ≡ ∃ y ∀ x P(x,y) |
| A. | yes |
| B. | no |
| Answer» B. no | |
| 55. |
Is ∀z S(x,y) a well-formed formula? |
| A. | yes |
| B. | no |
| Answer» A. yes | |
| 56. |
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» D. all of the mentioned | |
| 57. |
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» B. large branching factor. | |
| 58. |
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» C. set of goal predicates into individual subgoals | |
| 59. |
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» D. both a & b | |
| 60. |
Agent interacts with the world via _______________ and ______________ |
| A. | decision , effect |
| B. | perception, decision |
| C. | perception, action |
| D. | perception, effect |
| Answer» C. perception, action | |
| 61. |
The start node for search in plan space planning is_______________ |
| A. | bfs |
| B. | dfs |
| C. | both dfs and bfs |
| D. | a* |
| Answer» C. both dfs and bfs | |
| 62. |
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» A. forward chaining | |
| 63. |
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» C. a set of rules, the inference engine (ie), a working memory (wm) | |
| 64. |
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» D. all of the above | |
| 65. |
Which of the following are Informed search algorithms? |
| A. | best first search |
| B. | a* search |
| C. | iterative deeping search |
| D. | both a & b |
| Answer» D. both a & b | |
| 66. |
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» B. guaranteed | |
| 67. |
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» C. iterative deepening depth first search | |
| 68. |
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» D. h(n)<= h*(n) | |
| 69. |
The efficiency of A* algorithm depends on __________________________ |
| A. | depth |
| B. | the quality of heuristic |
| C. | unknown nodes |
| D. | d. none of the above |
| Answer» B. the quality of heuristic | |
| 70. |
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» A. when no successor of the node has better heuristic value. | |
| 71. |
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» D. all of the above | |
| 72. |
_______________________requires Linear Space but uses backtracking |
| A. | breadth first search |
| B. | recursive best first search (rbfs) |
| C. | a* |
| D. | ida* |
| Answer» B. recursive best first search (rbfs) | |
| 73. |
Which property asks that the algorithm is locally admissible? |
| A. | admissibility |
| B. | monotonicity |
| C. | informedness |
| D. | none of the above |
| Answer» B. monotonicity | |
| 74. |
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» B. finds the shortest path through the search space using the heuristic function i.e f(n)=g(n) + h(n) | |
| 75. |
Which is not problem in Hill climing? |
| A. | plateau |
| B. | ridges |
| C. | local maximum |
| D. | landscape |
| Answer» D. landscape | |
| 76. |
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» B. to escape the trap of local optimality. | |
| 77. |
Production/Rule looks like________________ |
| A. | pattern-->data |
| B. | action-->data |
| C. | pattern-->action |
| D. | none of the above |
| Answer» C. pattern-->action | |
| 78. |
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» C. by addition of extra node | |
| 79. |
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» B. finding consistent values for pairs of variables. | |
| 80. |
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» A. triple (s, g, o) | |
| 81. |
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» B. ordering links and casual link | |
| 82. |
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» 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. | |
| 83. |
What is the main advantage of backward state-space search? |
| A. | cost |
| B. | actions |
| C. | relevant actions |
| D. | all of the mentioned |
| Answer» C. relevant actions | |
| 84. |
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» B. start searching backwards from the goal | |
| 85. |
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» A. the regressed goal over action a when applied to goal g. | |
| 86. |
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» D. all of the above | |
| 87. |
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» C. denotes the set of positive effects of action a | |
| 88. |
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» A. the successor state s when action a is applied to state s. | |
| 89. |
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» D. both a & b | |
| 90. |
What arcs represents in AO Graph? |
| A. | subproblem to be solved individually |
| B. | solution |
| C. | path |
| D. | sequence of actions |
| Answer» A. subproblem to be solved individually | |
| 91. |
Which are the first AI applications of AO graph? |
| A. | saint |
| B. | xcon |
| C. | dendral |
| D. | both a and c |
| Answer» D. both a and c | |
| 92. |
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» C. both 1 and 2 | |
| 93. |
What cost is assumed for arc while solving AO* progress example? |
| A. | 0 |
| B. | 1 |
| C. | 2 |
| D. | 3 |
| Answer» B. 1 | |
| 94. |
What is the heuristic cost of SOLVED nodes in AO* example? |
| A. | 0 |
| B. | 1 |
| C. | 2 |
| D. | 3 |
| Answer» A. 0 | |
| 95. |
What is used to lable primitive problems in AO problem? |
| A. | unvisited |
| B. | unsolved |
| C. | solved |
| D. | visited |
| Answer» C. solved | |
Done Studing? Take A Test.
Great job completing your study session! Now it's time to put your knowledge to the test. Challenge yourself, see how much you've learned, and identify areas for improvement. Don’t worry, this is all part of the journey to mastery. Ready for the next step? Take a quiz to solidify what you've just studied.