UGC NET Dec 2023: Artificial Intelligence Questions and Answers with detailed explanation

Question: 84
What is the generic structure of Multi-Agent System (MAS)?

1. Single agent with multiple objectives
2. Multiagents with a single objective
3. Multiagents with diverse objectives and communication abilities
4. Multiagent with two objectives

Answer:

(3) Multiagents with diverse objectives and communication abilities

Explanation:

• Multi-Agent Systems (MAS) consist of multiple agents, each with their own goals and abilities. These agents interact and collaborate to achieve individual or collective objectives.
• Agents in MAS are typically autonomous, communicate with one another, and may have diverse objectives.
• The ability to communicate and coordinate is essential for solving complex problems that involve distributed systems or environments.

Other options are incorrect because:

1. MAS involves multiple agents, not a single agent.
2. MAS generally deals with agents having diverse, rather than a single, objective.
3. MAS is not restricted to only two objectives.

 Question:88
 

A _______ point of fuzzy set A is a point $x \in X$ at which $\mu_A(x) = 0.5$.

1. Core
2. Support
3. Crossover
4. $\alpha$-cut

Answer:

(3) Crossover

Explanation:

• A crossover point of a fuzzy set is a point where the membership function ($\mu_A(x)$) equals 0.5.
• This is a critical point often used in fuzzy logic systems to determine the boundary where the degree of membership is exactly halfway between full membership (1) and non-membership (0).

Other options are incorrect because:

1. Core refers to points where $\mu_A(x) = 1$ (full membership).
2. Support refers to points where $\mu_A(x) > 0$.
3. $\alpha$-cut is a subset of the fuzzy set where $\mu_A(x) \geq \alpha$ (not specific to 0.5).

 

Question:89
In a genetic algorithm optimization problem, the fitness function is defined as $f(x) = x^2 - 4x + 4$.
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. 2.25
2. 6.0
3. 0.0
4. 6.25

Solution:

The fitness function is $f(x) = x^2 - 4x + 4$
This can be rewritten as $f(x) = (x - 2)^2$
Since it is a minimization function (due to the square term), the individual with the smallest $f(x)$ value will have the best fitness.

Step-by-step Calculation:

1. For $x = 1.5$:
   $f(1.5) = (1.5 - 2)^2 = (-0.5)^2 = 0.25$

2. For $x = 2.0$:
   $f(2.0) = (2.0 - 2)^2 = (0)^2 = 0.0$

3. For $x = 3.0$:
   $f(3.0) = (3.0 - 2)^2 = (1.0)^2 = 1.0$

4. For $x = 4.5$:
   $f(4.5) = (4.5 - 2)^2 = (2.5)^2 = 6.25$

Best Fitness Value:

The smallest fitness value is $0.0$, which corresponds to $x = 2.0$.

Answer:

(3) 0.0

 

Question:90
In a feed forward neural network with the following specifications:

• Input layer has 4 neurons, hidden layer has 3 neurons, and output layer has 2 neurons.
• The network uses the sigmoid activation function for the given input values $[0.5, 0.8, 0.2, 0.6]$, as well as the initial weights for the connections:$$W_1 = [0.1, 0.3, 0.5, 0.2], \quad W_2 = [0.2, 0.4, 0.6, 0.2], \quad W_3 = [0.3, 0.5, 0.7, 0.2]$$

(Input layer to hidden layer weights)

$$W_4 = [0.4, 0.1, 0.3], \quad W_5 = [0.5, 0.2, 0.4]$$

(Hidden layer to output layer weights)

What is the output of the output layer when the given input values are passed through the neural network? Round the answer to two decimal places:

1. [0.62, 0.68]
2. [0.72, 0.78]
3. [0.82, 0.88]
4. [0.92, 0.98]

 Answer:

The final output is [0.63, 0.67], rounded to match (1) [0.62, 0.68].

 Explanation:

1. Input to Hidden Layer Calculations

The input vector is:

$$\text{Inputs} = [0.5, 0.8, 0.2, 0.6]$$

The weights for the hidden layer are:

$$W_1 = [0.1, 0.3, 0.5, 0.2], \quad W_2 = [0.2, 0.4, 0.6, 0.2], \quad W_3 = [0.3, 0.5, 0.7, 0.2]$$

For Hidden Neuron 1:

$$z_1 = W_1 \cdot \text{Inputs} = (0.1 \times 0.5) + (0.3 \times 0.8) + (0.5 \times 0.2) + (0.2 \times 0.6)$$

Apply the sigmoid function:

$$\text{Hidden Neuron 1 Output} = \sigma(z_1) = \frac{1}{1 + e^{-0.51}} \approx 0.625$$

For Hidden Neuron 2:

$$z_2 = W_2 \cdot \text{Inputs} = (0.2 \times 0.5) + (0.4 \times 0.8) + (0.6 \times 0.2) + (0.2 \times 0.6)$$ $$z_2 = 0.1 + 0.32 + 0.12 + 0.12 = 0.66$$

Apply the sigmoid function:

$$\text{Hidden Neuron 2 Output} = \sigma(z_2) = \frac{1}{1 + e^{-0.66}} \approx 0.659$$

For Hidden Neuron 3:

$$z_3 = W_3 \cdot \text{Inputs} = (0.3 \times 0.5) + (0.5 \times 0.8) + (0.7 \times 0.2) + (0.2 \times 0.6)$$ $$z_3 = 0.15 + 0.4 + 0.14 + 0.12 = 0.81$$

Apply the sigmoid function:

$$\text{Hidden Neuron 3 Output} = \sigma(z_3) = \frac{1}{1 + e^{-0.81}} \approx 0.692$$

The outputs of the hidden layer neurons are:

$$\text{Hidden Outputs} = [0.625, 0.659, 0.692]$$

2. Hidden Layer to Output Layer Calculations

The weights for the output layer are:

$$W_4 = [0.4, 0.1, 0.3], \quad W_5 = [0.5, 0.2, 0.4]$$

For Output Neuron 1:

$$z_4 = W_4 \cdot \text{Hidden Outputs} = (0.4 \times 0.625) + (0.1 \times 0.659) + (0.3 \times 0.692)$$ $$z_4 = 0.25 + 0.0659 + 0.2076 = 0.5235$$

Apply the sigmoid function:

$$\text{Output Neuron 1} = \sigma(z_4) = \frac{1}{1 + e^{-0.5235}} \approx 0.627$$

For Output Neuron 2:

$$z_5 = W_5 \cdot \text{Hidden Outputs} = (0.5 \times 0.625) + (0.2 \times 0.659) + (0.4 \times 0.692)$$ $$z_5 = 0.3125 + 0.1318 + 0.2768 = 0.7211$$

Apply the sigmoid function:

$$\text{Output Neuron 2} = \sigma(z_5) = \frac{1}{1 + e^{-0.7211}} \approx 0.672$$

3. Final Outputs

The final outputs of the neural network are:

$$\text{Outputs} = [0.63, 0.67] \, (\text{rounded to 2 decimal places})$$

 Question: 114
Which of the following are commonly used parsing techniques in NLP (Natural Language Processing) for syntactic analysis?

(A) Top-down parsing
(B) Bottom-up parsing
(C) Dependency parsing
(D) Statistical machine translation
(E) Earley parsing

Choose the correct answer from the options given below:

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

Answer:

(3) (A), (B), (C), (E) Only

Explanation:

• (A) Top-down parsing: A parsing technique that starts from the root of the parse tree and works down to the leaves.

• (B) Bottom-up parsing: A parsing technique that starts from the leaves of the parse tree and works up to the root.

• (C) Dependency parsing: Focuses on the dependencies between words in a sentence, identifying how words relate to each other.

• (E) Earley parsing: A dynamic programming parsing technique useful for context-free grammars.

• (D) Statistical machine translation: This is not a parsing technique but a method for translating text using probabilistic models.

Thus, the correct parsing techniques for syntactic analysis in NLP are (A), (B), (C), and (E).

Question: 115
In the context 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 certainty when the value of a node exceeds both the alpha and beta bounds.
(B) The primary purpose of Alpha-Beta pruning 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 bounds are initialized to negative and positive infinity, respectively, at the root node.

Choose the correct answer from the options given below:

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

 Answer:

(3) (A), (B), (D) Only

Explanation:

• (A) Alpha-Beta pruning can eliminate subtrees with certainty when the value of a node exceeds both the alpha and beta bounds.

  • Correct. Alpha-Beta pruning skips certain subtrees that cannot influence the final decision, saving computational resources.

• (B) The primary purpose of Alpha-Beta pruning is to save computation time by searching fewer nodes in the same tree.

  • Correct. The purpose of Alpha-Beta pruning is to reduce the number of nodes evaluated in the minimax algorithm, making it more efficient.

• (C) Alpha-Beta pruning guarantees the optimal solution in all cases by exploring the entire game tree.

  • Incorrect. Alpha-Beta pruning does not explore the entire tree; instead, it selectively evaluates nodes, though it still guarantees an optimal solution under the minimax strategy.

• (D) Alpha and Beta bounds are initialized to negative and positive infinity, respectively, at the root node.

  • Correct. At the root node, Alpha is initialized to $-\infty$ (lowest possible value), and Beta is initialized to $+\infty$ (highest possible value).

Question: 123
Match List - I with List - II.

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

Choose the correct answer from the options given below:

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

Explanation:

• (A) Greedy Best First Search → (II):
  Greedy Best First Search focuses only on the heuristic value, which can lead to incompleteness even in a finite search space.

• (B) A → (III):*
  A* is optimal and complete. If the optimal solution is reachable, it guarantees finding it; otherwise, it provides the best reachable solution.

• (C) Recursive Best First Search → (I):
  Recursive Best First Search is a memory-efficient variant of A* with space complexity $O(d)$, where $d$ is the depth of the search tree.

• (D) SMA → (IV):*
  Simplified Memory-bounded A* (SMA*) is designed for environments with limited memory and optimizes both computation and space complexity.

Thus, the correct matching is:
(A)-(II), (B)-(III), (C)-(I), (D)-(IV).

Question:125
Match List - I with List - II.

List - I	List - II
(A) Hill climbing	(I) $O(b^d)$
(B) Best first search	(II) $O(bd)$
(C) A* Search	(III) $O(1)$
(D) Depth first search	(IV) $O(b^m)$

Choose the correct answer from the options given below:

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

Answer:

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

Explanation:

1. (A) Hill Climbing → (I): $O(b^d)$

   • Hill climbing explores the search space by moving to neighboring nodes, and its complexity depends on the branching factor $b$ and the depth $d$ of the search tree.

2. (B) Best First Search → (IV): $O(b^m)$

   • Best First Search uses a priority queue for exploring nodes based on heuristic values. It can explore nodes up to a maximum depth $m$, with a branching factor $b$.

3. (C) A Search → (II): $O(bd)$*

   • A* Search is complete and optimal. It uses both the cost-so-far and the heuristic function, with complexity proportional to the branching factor $b$ and depth $d$.

4. (D) Depth First Search → (III): $O(1)$

   • Depth First Search requires very little memory, as it only stores nodes along the current path and backtracks when necessary.

Question:133
Arrange the following encoding strategies used in Genetic Algorithms (GAs) in the correct sequence starting from the initial step and ending with the final representation of solutions:

(A) Binary Encoding
(B) Real-Valued Encoding
(C) Permutation Encoding
(D) Gray Coding

Choose the correct answer from the options given below:

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

Answer:

(2) (B), (D), (A), (C)

Explanation:

1. (B) Real-Valued Encoding:

   • Typically used as an initial encoding technique in GAs when dealing with numerical problems.
   • Real numbers are directly used to represent solutions.

2. (D) Gray Coding:

   • Converts real values into binary sequences, minimizing errors during decoding. Gray coding ensures only one bit changes between successive numbers.

3. (A) Binary Encoding:

   • One of the most commonly used forms of encoding in GAs. It represents solutions as binary strings (e.g., 0s and 1s), which can be processed using crossover and mutation.

4. (C) Permutation Encoding:

   • Used when the solution involves ordered data, such as scheduling or sequencing problems, where order matters.

Question:135
Arrange the following steps in the correct sequence for applying an unsupervised learning technique such as K-means clustering to a dataset:

(A) Randomly initialize cluster centroids
(B) Assign each data point to the nearest cluster centroid
(C) Update the cluster centroids based on the mean of data points assigned to each cluster
(D) Specify the number of clusters (K) to partition the data into
(E) Repeat steps B and C until convergence criteria are met

Choose the correct answer from the options given below:

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

Answer:

(1) (D), (A), (B), (C), (E)

Explanation:

The K-means clustering algorithm follows these steps:

1. (D) Specify the number of clusters (K):
   The user defines the number of clusters to partition the data into. This is a prerequisite step for K-means.

2. (A) Randomly initialize cluster centroids:
   Random initial centroids are selected for the clusters.

3. (B) Assign each data point to the nearest cluster centroid:
   Each data point is assigned to the cluster represented by the nearest centroid based on a distance metric (e.g., Euclidean distance).

4. (C) Update the cluster centroids:
   The centroids are updated by calculating the mean of all the data points assigned to each cluster.

5. (E) Repeat steps B and C until convergence criteria are met:
   Steps (B) and (C) are repeated iteratively until the centroids no longer change significantly or the maximum number of iterations is reached.