UGC NET Computer Science Artificial Intelligence Previous Year Questions (PYQs)

All 33 Artificial Intelligence PYQs

Ordered newest exam first. Each question links back to the full paper it came from.

1. Question 1

   UGC NET January 2025

   What is the correct sequence of steps used in knowledge-base design?

   A. Ask questions about the intended interpretation.
   B. Choose the task domain or world to represent.
   C. Select atoms to represent propositions of interest.
   D. Tell the system propositions that are true in the intended interpretation.

   1. AA, B, C, D
   2. BB, C, D, A
   3. CC, D, A, B
   4. DD, A, B, C

   Answer: (B) B, C, D, A

   Explanation

   Knowledge engineering starts by identifying the task and world, then selecting vocabulary, axiomatizing known facts, and finally asking questions to test the intended interpretation. The correct option follows from the stated definition or calculation; the other options omit a necessary condition or describe a different concept.

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2. Question 2

   UGC NET January 2025

   Which are correct for a neural network?

   A. Training time depends on network size.
   B. A neural network can be simulated on a conventional computer.
   C. A neural network mimics the way the human brain works.
   D. A neural network includes feedback.

   1. AA and B only
   2. BA, C and D only
   3. CA, B and C only
   4. DA and C only

   Answer: (B) A, C and D only

   Explanation

   Training cost grows with model size, networks can run on conventional computers, and they are brain-inspired abstractions. Feedback is not present in every neural-network architecture. The correct option follows from the stated definition or calculation; the other options omit a necessary condition or describe a different concept.

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3. Question 3

   UGC NET January 2025

   Which is not a component of the classic planning definition?

   1. AInit
   2. BDomain
   3. CAction
   4. DGoal

   Answer: (B) Domain

   Explanation

   A classical planning problem specifies an initial state, actions and a goal. The domain supplies the model but is not one of the basic components named in this formulation. The correct option follows from the stated definition or calculation; the other options omit a necessary condition or describe a different concept.

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4. Question 4

   UGC NET January 2025

   Which one of the following is not related to feed-forward networks in the backpropagation algorithm?

   1. ABoolean function
   2. BContinuous function
   3. CArbitrary function
   4. DGreedy function

   Answer: (D) Greedy function

   Explanation

   Feed-forward neural networks are commonly discussed as function approximators for Boolean, continuous and arbitrary functions. “Greedy function” is not such a network-function category. The correct option follows from the stated definition or calculation; the other options omit a necessary condition or describe a different concept.

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5. Question 5

   UGC NET January 2025

   Definitions organised into the four categories Thinking Humanly, Thinking Rationally, Acting Humanly and Acting Rationally describe

   1. AMachine Learning
   2. BDeep Learning
   3. CArtificial Intelligence
   4. DNeural Network

   Answer: (C) Artificial Intelligence

   Explanation

   These four viewpoints are the classic framework used to define artificial intelligence: human-like versus rational thinking and acting. The correct option follows from the stated definition or calculation; the other options omit a necessary condition or describe a different concept.

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6. Question 6

   UGC NET January 2025

   Match List-I with List-II.

   List-I	List-II
   A. Decision tree B. Supervised learning C. Artificial neural network D. Instance-based learning	I. Delta learning rule II. Self-organizing map III. C4.5 algorithm IV. Nonlinear regression algorithm

   1. AA-I, B-II, C-III, D-IV
   2. BA-II, B-III, C-IV, D-I
   3. CA-III, B-IV, C-I, D-II
   4. DA-IV, B-I, C-II, D-III

   Answer: (C) A-III, B-IV, C-I, D-II

   Explanation

   Decision trees use C4.5, supervised learning includes nonlinear regression, artificial neural networks use the delta learning rule, and instance-based learning includes self-organizing maps in the supplied matching.

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7. Question 7

   UGC NET August 2024

   Match List-I with List-II.

   List-I	List-II
   (A) The activation function	(I) is called the delta rule.
   (B) The learning method of perceptron	(II) is one of the key components of the perceptron, as in the most common neural network architecture.
   (C) Areas of application of artificial neural networks	(III) is always boolean, like a switch.
   (D) The output of the perceptron	(IV) system identification and control.

   1. A(A)-(II), (B)-(IV), (C)-(III), (D)-(I)
   2. B(A)-(IV), (B)-(III), (C)-(II), (D)-(I)
   3. C(A)-(II), (B)-(I), (C)-(IV), (D)-(III)
   4. D(A)-(III), (B)-(IV), (C)-(II), (D)-(I)

   Answer: (C) (A)-(II), (B)-(I), (C)-(IV), (D)-(III)

   Explanation

   The activation function is one of the key components of a perceptron, so (A)-(II). A perceptron learns by the delta rule, giving (B)-(I). Artificial neural networks are applied to tasks such as system identification and control, so (C)-(IV). A perceptron emits a boolean, switch-like output, so (D)-(III). Only option C carries all four pairings.

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8. Question 8

   UGC NET August 2024

   Arrange the following steps in proper sequence
   involved in a Genetic Algorithm :
   A. Selection
   B. Initialization
   C. Crossover
   D. Mutation
   E. Evaluation

   1. A(A), (B), (C), (D), (E)
   2. B(E), (A), (B), (D), (C)
   3. C(B), (E), (A), (C), (D)
   4. D(A), (C), (B), (D), (E)

   Answer: (C) (B), (E), (A), (C), (D)

   Explanation

   A genetic algorithm initialises a population (B), evaluates each individual’s fitness (E), selects parents according to that fitness (A), recombines them by crossover (C), and finally perturbs the offspring by mutation (D). Selection needs fitness scores, so evaluation must come before it. That order is option C.

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9. Question 9

   UGC NET August 2024

   Arrange the following steps in a proper sequence
   for the process of training a neural network.
   A. Weight initialization
   B. Feed forward
   C. Back Propagation
   D. Loss Calculation
   E. Weight Update

   1. A(A), (B), (D), (C), (E)
   2. B(D), (B), (A), (C), (E)
   3. C(A), (C), (D), (B), (E)
   4. D(E), (C), (B), (D), (A)

   Answer: (A) (A), (B), (D), (C), (E)

   Explanation

   Training runs: initialise the weights (A), feed the input forward to obtain predictions (B), compute the loss against the targets (D), back-propagate the gradients (C), then update the weights (E). The loss must exist before gradients can be taken, so (D) precedes (C). That order is option A.

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10. Question 10

    UGC NET August 2024

    Match List-I with List-II.

    List-I	List-II
    (A) Natural language processing	(I) A method of training algorithm by rewarding desired behaviour and/or punishing undesired one.
    (B) Reinforcement learning	(II) System designed to emulate the decision making abilities of a human expert.
    (C) Support vector machine	(III) A branch of AI focused on understanding and generating human language.
    (D) Expert system	(IV) A machine learning technique that finds the hyperplane that best separates different classes in a feature space.

    1. A(A)-(I), (B)-(II), (C)-(IV), (D)-(III)
    2. B(A)-(III), (B)-(II), (C)-(I), (D)-(IV)
    3. C(A)-(III), (B)-(I), (C)-(IV), (D)-(II)
    4. D(A)-(II), (B)-(IV), (C)-(III), (D)-(I)

    Answer: (C) (A)-(III), (B)-(I), (C)-(IV), (D)-(II)

    Explanation

    Natural language processing is the AI branch for understanding and generating human language, so (A)-(III). Reinforcement learning trains by rewarding desired and punishing undesired behaviour, giving (B)-(I). A support vector machine finds the separating hyperplane, so (C)-(IV). An expert system emulates a human expert’s decisions, so (D)-(II). Option C is correct.

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11. Question 11

    UGC NET August 2024

    What is the role of Back Propagation
    Algorithm?

    Artificial Neural Networks (ANNs) are computational models inspired by the human brain’s neural networks. They consist of interconnected nodes, or neurons, organized into layers: an input layer, one or more hidden layers and an output layer. Each connection between neurons has a weight that adjusts as learning progresses, allowing the network to adapt and improve its performance. ANNs are particularly effective in recognizing patterns, making them valuable for tasks such as image and speech recognition, natural language processing and predictive analytics. Learning in ANNs typically involves training algorithms like back propagation, which minimize the error by adjusting the weights. As a subset of machine learning, ANNs have revolutionized the field of Artificial Intelligence by providing solutions to complex problems that traditional algorithms struggle with.

    1. ATo reduce error
    2. BTo secure network
    3. CTo control speed of data
    4. DTo add different layers

    Answer: (A) To reduce error

    Explanation

    Back-propagation computes the gradient of the loss with respect to every weight by applying the chain rule backwards through the network, so that the weights can then be adjusted to reduce the error. Its purpose is error reduction, not securing the network, controlling data speed or adding layers. Option A is correct.

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12. Question 12

    UGC NET August 2024

    What is the role of weights in an ANN ?

    Artificial Neural Networks (ANNs) are computational models inspired by the human brain’s neural networks. They consist of interconnected nodes, or neurons, organized into layers: an input layer, one or more hidden layers and an output layer. Each connection between neurons has a weight that adjusts as learning progresses, allowing the network to adapt and improve its performance. ANNs are particularly effective in recognizing patterns, making them valuable for tasks such as image and speech recognition, natural language processing and predictive analytics. Learning in ANNs typically involves training algorithms like back propagation, which minimize the error by adjusting the weights. As a subset of machine learning, ANNs have revolutionized the field of Artificial Intelligence by providing solutions to complex problems that traditional algorithms struggle with.

    1. ATo store data
    2. BTo adjust and improve network performance
    3. CTo control the speed
    4. DTo secure the network

    Answer: (B) To adjust and improve network performance

    Explanation

    The weight on each connection scales the signal passing along it, and learning consists precisely of adjusting these weights so the network’s predictions improve. They are trainable parameters, not a data store, a speed control or a security mechanism. Option B is correct.

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13. Question 13

    UGC NET August 2024

    Which of the following is/are the
    application area(s) of ANN ?
    A. Natural Language Processing
    B. Image Processing
    C. Pattern Recognition
    D. Speech Recognition
    Choose the correct answer from the
    options given below :

    Artificial Neural Networks (ANNs) are computational models inspired by the human brain’s neural networks. They consist of interconnected nodes, or neurons, organized into layers: an input layer, one or more hidden layers and an output layer. Each connection between neurons has a weight that adjusts as learning progresses, allowing the network to adapt and improve its performance. ANNs are particularly effective in recognizing patterns, making them valuable for tasks such as image and speech recognition, natural language processing and predictive analytics. Learning in ANNs typically involves training algorithms like back propagation, which minimize the error by adjusting the weights. As a subset of machine learning, ANNs have revolutionized the field of Artificial Intelligence by providing solutions to complex problems that traditional algorithms struggle with.

    1. A(A) and (B) Only
    2. B(B) and (C) Only
    3. C(A), (B) and (C) Only
    4. D(A), (B), (C) and (D)

    Answer: (D) (A), (B), (C) and (D)

    Explanation

    The passage lists pattern recognition together with image recognition, speech recognition, natural language processing and predictive analytics among the tasks artificial neural networks handle well. All four listed areas therefore apply, so option D is correct.

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14. Question 14

    UGC NET August 2024

    Artificial Neural Networks (ANNs) are
    inspired by :

    Artificial Neural Networks (ANNs) are computational models inspired by the human brain’s neural networks. They consist of interconnected nodes, or neurons, organized into layers: an input layer, one or more hidden layers and an output layer. Each connection between neurons has a weight that adjusts as learning progresses, allowing the network to adapt and improve its performance. ANNs are particularly effective in recognizing patterns, making them valuable for tasks such as image and speech recognition, natural language processing and predictive analytics. Learning in ANNs typically involves training algorithms like back propagation, which minimize the error by adjusting the weights. As a subset of machine learning, ANNs have revolutionized the field of Artificial Intelligence by providing solutions to complex problems that traditional algorithms struggle with.

    1. AQuantum mechanics
    2. BHuman brain's neural network
    3. CComputer Hardware architecture
    4. DGenetic algorithm

    Answer: (B) Human brain's neural network

    Explanation

    Artificial neural networks are computational models inspired by the human brain’s neural networks: interconnected nodes, or neurons, arranged in layers with weighted connections that adjust as learning progresses. Option B is correct.

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15. Question 15

    UGC NET August 2024

    Which of the following layers may be more
    than one in number ?

    Artificial Neural Networks (ANNs) are computational models inspired by the human brain’s neural networks. They consist of interconnected nodes, or neurons, organized into layers: an input layer, one or more hidden layers and an output layer. Each connection between neurons has a weight that adjusts as learning progresses, allowing the network to adapt and improve its performance. ANNs are particularly effective in recognizing patterns, making them valuable for tasks such as image and speech recognition, natural language processing and predictive analytics. Learning in ANNs typically involves training algorithms like back propagation, which minimize the error by adjusting the weights. As a subset of machine learning, ANNs have revolutionized the field of Artificial Intelligence by providing solutions to complex problems that traditional algorithms struggle with.

    1. AInput layer
    2. BHidden layer
    3. COutput layer
    4. DPhysical layer

    Answer: (B) Hidden layer

    Explanation

    A network has exactly one input layer and one output layer, but it may stack one or more hidden layers between them — that depth is what lets it model complex relationships. The physical layer belongs to networking models, not to artificial neural networks. Option B is correct.

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16. Question 16

    UGC NET December 2023

    What is the generic structure of Multi Agent System (MAS) ?

    1. ASingle agent with multiple objectives
    2. BMultiagents with a single objectives
    3. CMultiagents with diverse objectives and communication abilities
    4. DMultiagent with two objectives

    Answer: (C) Multiagents with diverse objectives and communication abilities

    Explanation

    The key idea is stated in the problem: What is the generic structure of Multi Agent System (MAS) ?. The correct answer is C: Multiagents with diverse objectives and communication abilities. It follows the governing definition, algorithm, or calculation in this artificial intelligence item; the other choices fail to satisfy one or more stated conditions.

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17. Question 17

    UGC NET December 2023

    A point of fuzzy set A is a point xeX at which wA(x)=0.5

    1. ACore
    2. BSupport
    3. CCrossover
    4. Da-—cut

    Answer: (C) Crossover

    Explanation

    The key idea is stated in the problem: A point of fuzzy set A is a point xeX at which wA(x)=0.5. The correct answer is C: Crossover. It follows the governing definition, algorithm, or calculation in this artificial intelligence item; the other choices fail to satisfy one or more stated conditions.

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18. Question 18

    UGC NET December 2023

    In a genetic algorithm optimization problem the fitness function is defined as f(x) =x?- 4x +4. Given a population of four individuals with values of x : {1.5, 2.0, 3.0, 4.5} What is the fitness value of the individual that will be selected as the parent for reproduction in one generation ?

    1. A25
    2. B0
    3. C0
    4. D25

    Answer: (D) 25

    Explanation

    The key idea is stated in the problem: In a genetic algorithm optimization problem the fitness function is defined as f(x) =x?- 4x +4. Given a population of four individuals with values of x : {1.5, 2.0, 3.0, 4.5} What is the fitness value of the individual that will be selected as the parent for r. The correct answer is D: 25. It follows the governing definition, algorithm, or calculation in this artificial intelligence item; the other choices fail to satisfy one or more stated conditions.

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19. Question 19

    UGC NET December 2023

    In the content of Alpha Beta pruning in game trees which of the following statements are correct regarding cut off procedures ?
    A. Alpha Beta pruning can eliminate subtrees with certainly when the value of a node exceeds both the alpha and beta bonds.
    B. The primarily purpose of Alpha-Beta proning is to save computation time by searching fewer nodes in the same tree.
    C. Alpha Beta pruning guarantees the optimal solution in all cases by exploring the entire game tree.
    D. Alpha and Beta bonds are initialized to negative and positive infinity respectively at the root note. Choose the correct answer from the options given below :

    1. A(A), ©), () Only
    2. B(B), (C), (DB) Only
    3. C(A), (B), (DB) Only
    4. D(©, (B) Only

    Answer: (C) (A), (B), (DB) Only

    Explanation

    The key idea is stated in the problem: In the content of Alpha Beta pruning in game trees which of the following statements are correct regarding cut off procedures ?
    A. Alpha Beta pruning can eliminate subtrees with certainly when the value of a node exceeds both the alpha and beta bonds.
    B. The. The correct answer is C: (A), (B), (DB) Only. It follows the governing definition, algorithm, or calculation in this artificial intelligence item; the other choices fail to satisfy one or more stated conditions.

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20. Question 20

    UGC NET December 2023

    Match List-I with List-II.

    Source matching prompt	Source values
    See question stem	See answer choices

    1. A(A), (B)-(), (C)-C), (D)-(IV)
    2. B(A), (B)-@, (©), (D)-(IV)
    3. C(A)-(1, (B)-@, (C)-(V), (D)-C)
    4. D(A)-(0), (B)-(), (C)-(IV), (D)-

    Answer: (D) (A)-(0), (B)-(), (C)-(IV), (D)-

    Explanation

    The key idea is stated in the problem: Match List - I with List - II. List - I List - II
    A. oN {b}= OQ ¢
    B. {6}. {6} = () {4} (CS) th, {b}}-b= (IT) {{}}
    D. $U {l}}= (IV) {, {}} Choose the correct answer from the options given below :. The correct answer is D: (A)-(0), (B)-(), (C)-(IV), (D)-. It follows the governing definition, algorithm, or calculation in this artificial intelligence item; the other choices fail to satisfy one or more stated conditions.

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21. Question 21

    UGC NET December 2023

    Which of the following constrains when formulating the LPP ?

    Food X contains 6 units of Vitamin D per gram and 7 units of Vitamin E per gram and costs Rs. 12 per gram. Food Y contains 8 units of Vitamin D per gram and 12 units of Vitamin E per gram and costs Rs. 20 per gram. The daily minimum requirements of vitamin D and vitamin E are 100 units and 120 units respectively. Let x and y be the quantities in grams of Food X and Food Y.

    1. A6x+7y<100, 8x+12y<120, x,y 20
    2. B6x+8y<100, 7x+12y<120, x,y20
    3. C6x+7y 2100, 8x+12y>120, x,y>0
    4. D6x+8y 2100, 7x+12y 2120, x,y20

    Answer: (D) 6x+8y 2100, 7x+12y 2120, x,y20

    Explanation

    The key idea is stated in the problem: Which of the following constrains when formulating the LPP ?. The correct answer is D: 6x+8y 2100, 7x+12y 2120, x,y20. It follows the governing definition, algorithm, or calculation in this artificial intelligence item; the other choices fail to satisfy one or more stated conditions.

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22. Question 22

    UGC NET December 2023

    Which of the following are quantities (in grams) of food X and Y respectively when the cost of food is minimum : 1

    Food X contains 6 units of Vitamin D per gram and 7 units of Vitamin E per gram and costs Rs. 12 per gram. Food Y contains 8 units of Vitamin D per gram and 12 units of Vitamin E per gram and costs Rs. 20 per gram. The daily minimum requirements of vitamin D and vitamin E are 100 units and 120 units respectively. Let x and y be the quantities in grams of Food X and Food Y.

    1. AOand 125 5
    2. B15 and 7 P 120 49
    3. C7 an
    4. D0 and 10

    Answer: (B) 15 and 7 P 120 49

    Explanation

    The key idea is stated in the problem: Which of the following are quantities (in grams) of food X and Y respectively when the cost of food is minimum : 1. The correct answer is B: 15 and 7 P 120 49. It follows the governing definition, algorithm, or calculation in this artificial intelligence item; the other choices fail to satisfy one or more stated conditions.

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23. Question 23

    UGC NET December 2023

    The dual of the formulated LPP is :

    Food X contains 6 units of Vitamin D per gram and 7 units of Vitamin E per gram and costs Rs. 12 per gram. Food Y contains 8 units of Vitamin D per gram and 12 units of Vitamin E per gram and costs Rs. 20 per gram. The daily minimum requirements of vitamin D and vitamin E are 100 units and 120 units respectively. Let x and y be the quantities in grams of Food X and Food Y.

    1. AMax Z = 100u + 120v; 6u + 7v ≤ 12; 8u + 12v ≤ 20; u, v ≥ 0
    2. BMax Z = 12u + 20v; 6u + 7v ≤ 100; 8u + 12v ≤ 120; u, v ≥ 0
    3. CMax Z = 100u + 120v; 6u + 7u ≤ 12; 8u + 7v ≤ 20; u, v unrestricted
    4. DMax Z = 100u + 120u; 6u + 7v ≥ 12; 8u + 12v ≥ 20; u, v ≥ 0

    Answer: (A) Max Z = 100u + 120v; 6u + 7v ≤ 12; 8u + 12v ≤ 20; u, v ≥ 0

    Explanation

    The key idea is stated in the problem: The dual of the formulated LPP is :. The correct answer is A: Max Z = 100u + 120v; 6u + 7v ≤ 12; 8u + 12v ≤ 20; u, v ≥ 0. It follows the governing definition, algorithm, or calculation in this artificial intelligence item; the other choices fail to satisfy one or more stated conditions.

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24. Question 24

    UGC NET December 2023

    Match List-I with List-II.

    Source matching prompt	Source values
    See question stem	See answer choices

    1. A(A)-C, @)-€V), ()-0), (P)-Ca)
    2. B(A)-CV), (B)-CM), ()-@), (P)-
    3. C(A)-CV), (BN), (C)-(), (P)-(IN)
    4. D(A)-(IID), (B)-(IV), (C)-(), (D)-()

    Answer: (B) (A)-CV), (B)-CM), ()-@), (P)-

    Explanation

    The key idea is stated in the problem: Match List - I with List - II. List - I List - IT
    A. SZA () Increment M and skip it zero
    B. SKI (II) Skip if AC is negative
    C. SNA (III) Skip if input flag is on
    D. ISZ (IV) Skip if AC is Zero Choose the correct answer from the options given below :. The correct answer is B: (A)-CV), (B)-CM), ()-@), (P)-. It follows the governing definition, algorithm, or calculation in this artificial intelligence item; the other choices fail to satisfy one or more stated conditions.

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25. Question 25

    UGC NET November 2020

    The process of removing details from a given state representation is called

    1. AExtraction
    2. BMining
    3. CSelection
    4. DAbstraction

    Answer: (D) Abstraction

    Explanation

    Abstraction deliberately suppresses irrelevant detail and retains features needed for reasoning. It is used to make a state space or problem representation manageable.

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26. Practise Artificial Intelligence in a timed set

27. Question 26

    UGC NET November 2020

    Which statement is NOT true of problem solving in artificial intelligence?

    1. AIt implements heuristic search techniques.
    2. BSolution steps are not explicit.
    3. CKnowledge is imprecise.
    4. DIt works on or implements a repetition mechanism.

    Answer: (D) It works on or implements a repetition mechanism.

    Explanation

    AI problem-solving systems commonly use heuristic search and may handle incomplete or imprecise knowledge. A repetition mechanism is not a defining property of problem solving; it would often signal an undesirable loop.

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28. Question 27

    UGC NET November 2020

    What kind of clauses occur in conjunctive normal form?

    1. ADisjunctions of literals
    2. BDisjunctions of variables
    3. CConjunctions of literals
    4. DConjunctions of variables

    Answer: (A) Disjunctions of literals

    Explanation

    CNF is a conjunction of clauses, and each clause is a disjunction of literals. A literal may be a variable or its negation, so “variables” alone is too restrictive.

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29. Question 28

    UGC NET November 2020

    Which of the following statements are true?
    A. Asentence a entails another sentence f if 8 is true in few worlds where @ is true.
    B. Forward chaining and backward chaining are very natural reasoning algorithms for
    knowledge bases in Horn form.
    C. Sound inference algorithms derive all sentences that are entailed.
    D. Propositional logic does not scale to environments of unbounded size.

    1. AOCR review required
    2. BOCR review required
    3. C(C) and (D) only (4) (B) and (D) only
    4. DOCR review required

    Answer: (D) OCR review required

    Explanation

    Entailment requires truth in every model where the premise is true, not merely some models. Horn-clause chaining is natural, and propositional logic does not scale to unbounded domains; hence B and D are true.

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30. Question 29

    UGC NET November 2020

    Which of the following statements are true?
    A. Minimax search is breadth-first; it processes all the nodes at a level before moving to
    a node in next level.
    B. The effectiveness of the alpha-beta pruning is highly dependent on the order in which
    the states are examined.
    C. The alpha-beta search algorithm computes the same optimal moves as minimax
    algorithm.
    D. Optimal play in games of imperfact information does not require reasoning about the
    current and future belief states of each player.

    1. AOCR review required
    2. BOCR review required
    3. C(B) and (C) only (4) (C) and (D) only
    4. DOCR review required

    Answer: (C) (B) and (C) only (4) (C) and (D) only

    Explanation

    Minimax is normally depth-first rather than breadth-first. Alpha-beta pruning depends strongly on move ordering and returns the same optimal move as minimax; imperfect-information games require reasoning about belief states.

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31. Question 30

    UGC NET November 2020

    Match List I with List II
    List I
    A. Branch-and-bound
    B. (©)
    D. Steepest-accent hill climbing

    Constraint satisfaction

    Means-end-analysis

    @)

    aD

    (ly
    Statement IV: List IT

    Keeps track of all partial paths which
    can be candidate for further
    exploration.

    Detects difference between current
    state and goal state

    Discovers problem state(s) that satisfy
    a set of constraints.

    Considers all moves from current state
    and selects best move

    List I	List II
    Items are listed in the question stem.	Match each item to its stated description or complexity.

    1. AOCR review required
    2. BOCR review required
    3. COCR review required
    4. DOCR review required

    Answer: (A) OCR review required

    Explanation

    Branch-and-bound retains candidate partial paths; steepest-ascent chooses the best neighbouring move; constraint satisfaction finds states satisfying constraints; means–ends analysis reduces differences from the goal.

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32. Question 31

    UGC NET November 2020

    Match List I with List II
    List I List II
    A. Greedy Best-First Search () Space complexity is O(d) where
    d=depth of the deepest optimal
    solution
    B. A* (I) Incomplete even if the search space is
    finite.
    C. Recursive Best-First Search (I) Optimal if optimal solution is
    reachable; otherwise, returns the best
    reachable optimal solution.
    D. SMA* (IV) Computation and space complexity is
    too high.

    List I	List II
    Items are listed in the question stem.	Match each item to its stated description or complexity.

    1. AOCR review required
    2. BOCR review required
    3. CA-III, B-II, C-IV, D-I (4) A-III. B-IV. C-II, D-I
    4. DOCR review required

    Answer: (A) OCR review required

    Explanation

    Greedy best-first search can be incomplete, A* can have prohibitive time and space costs, recursive best-first search uses linear-depth space, and SMA* returns the best reachable solution when memory is limited.

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33. Question 32

    UGC NET November 2020

    Given below are two statements:
    If two variables V, and V; are used for clustering, then consider the following statements for
    k means clustering with k = 3:-
    Statement I: If V, and V2 have correlation of 1 the cluster centroid will be in straight line.
    Statement II: IfV, and V2 have correlation of 0 the cluster centroid will be in straight line.
    In the light of the above statements, choose the correct answer from the options given below
    ce) Both Statement I and Statement II are true

    1. AOCR review required
    2. BBoth Statement I and Statement II are false
    3. CStatement I is correct but Statement IT is false
    4. DStatement I is incorrect but Statement IT is true.

    Answer: (C) Statement I is correct but Statement IT is false

    Explanation

    Perfect correlation places the data and therefore the k-means centroids along one straight-line relation. Zero correlation does not imply such collinearity, so statement I alone is true.

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34. Question 33

    UGC NET November 2020

    Given below are two statements:

    Statement I: A genetic algorithm is a stochastic hill-climbing search in which a large

    population of states is maintained.

    Statement II: In nondeterministic environments. agents can apply AND-OR search to

    generate contingent plans that reach the goal regardless of which outcomes
    occur during execution.

    In the light of the above statements, choose the Correct answer from the options given below

    qa)

    1. AOCR review required
    2. BOCR review required
    3. COCR review required
    4. DOCR review required

    Answer: (A) OCR review required

    Explanation

    A genetic algorithm keeps a population and uses stochastic selection and variation. AND–OR search represents an agent’s alternatives and uncertain outcomes, enabling a contingent plan that succeeds for every considered outcome.

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