Sr.

No Question

1 Who is considered the father of Artificial Intelligence?

2 What is the goal of Artificial Intelligence?

3 Which of the following is NOT a branch of AI?

4 In problem-solving, what is the term for the initial state?

Which algorithm guarantees finding the shortest path in

5
an unweighted graph?

What type of search uses a heuristic function to

6
estimate the cost of reaching the goal?

7 The 8-puzzle problem is an example of which type of

problem?

8 What is the main disadvantage of Depth-First Search

(DFS)?

9 Which search strategy expands the node that is closest

to the goal?

10 Hill Climbing algorithm may fail due to:

11 Which of the following is an uninformed search
strategy?

12 A well-defined problem should have:

Which algorithm uses both the cost to reach the node

13 and an estimate of the cost to reach the goal?

In a production system, rules are stored in which of the

14 following components?

Which of the following is a constraint satisfaction

15
problem?

16 What does BFS stand for in Artificial Intelligence?

Which method allows reasoning about uncertainty in

17
AI?

18 The probability of event B given event A is called:

19 Bayes' theorem is used to calculate:

20 Which search algorithm is optimal and complete?

21 What is the main advantage of Bidirectional Search?

22 Which of the following is NOT a problem characteristic
in AI?

23 Forward reasoning is also called:

Which search technique repeatedly explores deeper

24 into the search tree without exceeding a given depth?

In propositional logic, what is a statement that is either

25 true or false?

In Predicate logic, which symbol represents "There

26
exists"?

Which of the following techniques is used in constraint

27
satisfaction problems?

28 The Heuristic function is used to:

In logic programming, Prolog is a language based on

29
which type of reasoning?

30 The Dempster-Shafer theory is primarily used for

reasoning about:
 Answers (A/B/C/D)

A) Alan Turing B) John McCarthy C) Marvin Minsky D) Arthur Samuel

A) To replace humans B) To create machines that can think like humans C) To

develop advanced computers D) To solve complex math problems

A) Machine Learning B) Robotics C) Neural Networks D) Classical Mechanics

A) Goal state B) Solution space C) Start state D) Search space

A) Depth-First Search B) Breadth-First Search C) Hill Climbing D) Best-First Search

A) A* Search B) Breadth-First Search C) Depth-First Search D) Bidirectional Search

A) State-space search B) Constraint Satisfaction Problem C) Optimization Problem

D) Game Theory Problem

A) High time complexity B) May not find the optimal solution C) High memory usage
D) Slow speed

A) Depth-First Search B) Breadth-First Search C) Best-First Search D) Bidirectional

Search

A) Local maxima B) Global maxima C) Incomplete solution D) Infinite loops

A) A* Search B) Best-First Search C) Breadth-First Search D) Simulated Annealing

A) No solution B) Infinite states C) Clearly defined goal and operators D) Ambiguous

goal

A) Best-First Search B) A* Search C) Depth-First Search D) Breadth-First Search

A) Working memory B) Knowledge base C) Control mechanism D) Problem

generator

A) 8-puzzle problem B) N-Queens problem C) Missionaries and Cannibals D)

Dijkstra’s algorithm

A) Backward First Search B) Basic First Search C) Breadth First Search D) Best First
Search

A) Propositional logic B) Probability theory C) First-order logic D) Decision trees

A) Joint probability B) Marginal probability C) Conditional probability D) Posterior

probability

A) Prior probability B) Posterior probability C) Likelihood D) Joint probability

A) Hill Climbing B) Simulated Annealing C) A* Search D) DFS

A) Less memory usage B) Faster time to find a solution C) Guaranteed optimal

solution D) Simplicity
A) Problem complexity B) Problem size C) Problem structure D) Problem difficulty

A) Backtracking B) Goal-driven reasoning C) Data-driven reasoning D) Reverse

reasoning

A) Depth-First Search B) Iterative Deepening C) Breadth-First Search D) Hill Climbing

A) Proposition B) Predicate C) Constant D) Variable

A) ∀ B) ∃ C) ¬ D) ∧

A) A* Search B) Best-First Search C) Backtracking D) Hill Climbing

A) Estimate the total cost from the start state B) Calculate the exact path cost C)
Estimate the cost to reach the goal state D) Find the shortest path

A) Forward reasoning B) Backward reasoning C) Probabilistic reasoning D) Fuzzy

reasoning

A) Probabilities B) Certainty C) Belief and doubt D) Uncertainty

 Correct Option

A
C

B
D

C
Sr. No Question

1 What is the primary purpose of knowledge

representation?

2 Which of the following is an example of

declarative knowledge?

3 Propositional logic deals with:

4 Predicate logic is more expressive than

propositional logic because it can:

5 The key difference between procedural and

declarative knowledge is:

6 Logic programming languages are based on:

7 Forward reasoning is:

8 Backward reasoning is often used in:

9 Bayesian networks are primarily used for:

 9 Bayesian networks are primarily used for:

10 Dempster-Shafer theory is used to:

11 Certainty factors are used in:

12 Rule-based systems operate on:

What does P∧QP \wedge QP∧Q represent

13 in propositional logic?

The probability of an event given prior

14
evidence is known as:

The result of Dempster's combination rule

15
is:

16 In a Bayesian network, nodes represent:

17 What is the output of backward reasoning?

 The Dempster-Shafer theory assigns belief
18 values to:

Which of the following statements is true

19 about certainty factors?

20 In forward reasoning, reasoning proceeds:

21 The Bayes' theorem combines prior and:

What kind of system typically uses rule-

22 based reasoning?

Which statement is false about predicate

23
logic?

24 A proposition is:

25 Bayesian networks use the concept of:

26 Dempster-Shafer theory differs from

probability theory by:
 Dempster-Shafer theory differs from
26 probability theory by:

27 The certainty factor is a measure of:

28 In propositional logic, P→Q means:

In Dempster-Shafer theory, uncertainty

29 refers to:

What does P∨Q represent in propositional

30
logic?

C
 Answers (A/B/C/D) Correct Option
A) To simulate reasoning
B) To store data efficiently
A
C) To reduce computation time
D) To analyze algorithms
A) Step-by-step instructions
B) Mathematical theorems
B
C) Car repair manuals
D) Diagnosing a patient
A) Objects
B) Relations
C
C) Truth values
D) Functions
A) Represent objects
B) Include variables
B
C) Use only constants
D) Be used for all reasoning
A) How the knowledge is represented
B) Where it is stored
A
C) The speed of processing
D) The complexity of algorithms
A) Object-oriented programming
B) Procedural knowledge
C
C) First-order logic
D) Second-order logic
A) A top-down approach
B) A bottom-up approach
B
C) Based on deduction
D) Always leads to accurate results
A) Diagnosis
B) Data mining
A
C) Clustering
D) Classification
A) Machine learning

B
B) Probabilistic inference
B
C) Image recognition
D) Graph theory
A) Assign probabilities
B) Combine evidence
B
C) Classify data
D) Prove theorems
A) Formal logic
B) Expert systems
B
C) Computer hardware
D) Mathematical modeling
A) Random data
B) Sets of predefined rules
B
C) Machine learning algorithms
D) Neural networks
A) Disjunction
B) Conjunction
B
C) Implication
D) Negation
A) Prior probability
B) Posterior probability
C
C) Conditional probability
D) Likelihood
A) A single probability
B) A belief function
B
C) A certainty factor
D) A logical rule
A) Functions
B) Variables
B
C) Probabilities
D) Data points
A) Facts
B) Conclusions
D
C) A set of axioms
D) Hypotheses
A) Probability distributions
B) Hypotheses
B
C) Data points
D) Rules
A) They can only be between 0 and 1
B) They quantify uncertainty
B
C) They are always additive
D) They represent physical properties
A) From conclusion to premise
B) From evidence to conclusion
B
C) From hypothesis to data
D) From axiom to fact
A) Posterior probabilities
B) Likelihoods
B
C) Predictive models
D) Truth tables
A) Neural networks
B) Expert systems
B
C) Regression analysis
D) Genetic algorithms
A) It can handle functions
B) It includes constants and variables
C
C) It cannot represent relations
D) It is used in artificial intelligence
A) A statement that is always false
B) A declarative sentence with a truth value
B
C) A variable in a function
D) An undefined term in logic
A) Directed acyclic graphs
B) Decision trees
A
C) Linear regression
D) Random walks
A) Using certainty factors
B) Allowing for belief in unknown hypotheses
B
C) Relying on forward reasoning
 B

D) Including decision theory

A) Data accuracy
B) Expert opinion
C
C) Confidence in a conclusion
D) Logical soundness
A) P or Q
B) P implies Q
B
C) P and Q
D) Not P
A) Evidence
B) Conflict
C
C) Lack of belief
D) Inconsistent data
A) Conjunction C
B) Negation
C) Disjunction
D) Implication
 Question
1. What is the primary objective of Alpha-Beta pruning in game playing?
a. Maximize player’s score
b. Minimize opponent's score
c. Reduce the number of nodes evaluated
d. Increase game complexity
2. In the Minimax algorithm, who is the minimizing player?
a. First player
b. Opponent
c. Maximizing player
d. A random player
3. What is the terminal node in a game tree?
a. The node with the highest value
b. The node with the lowest value
c. An intermediate node
d. Final outcome
4. Which of the following best describes a frame in AI?
a. A method for learning
b. A data structure for representing a stereotyped situation
c. A neural network training method
d. A probability calculation tool
5. What does a script in AI represent?
a. A predefined sequence of events
b. A random set of actions
c. A solution to a problem
d. A method for solving optimization problems
6. What is the purpose of the Alpha value in Alpha-Beta pruning?
a. The best value the maximizing player can guarantee
b. The best value the minimizing player can guarantee
c. The total depth of the game tree
d. The optimal solution value
7. What is a weakness of semantic networks?
a. They are too graphical
b. They are hard to visualize
c. Lack of ability to represent complex logic
d. Require extensive memory
8. Which of the following is an advantage of conceptual dependency theory?
a. Represents actions and events in a structured way
b. Generates probabilistic outcomes
c. Reduces ambiguity in natural language
d. Stores specific numeric data
9. What is the Minimax value of a terminal node?
a. It is always zero
b. The value at the terminal node itself
c. It depends on Alpha-Beta pruning
d. It is calculated during backpropagation
10. What is the key difference between frames and semantic networks?
a. Frames use graphs, while semantic networks are tabular
b. Semantic networks are time-based
c. Frames are structured data, while semantic nets are graphical
d. Frames and semantic nets are identical
11. In game-playing AI, why is it important to perform depth-limited search?
a. To prevent the game tree from growing too large
b. To make moves faster
c. To simulate human behavior
d. To ignore losing strategies
12. Which AI technique is used to capture causal relationships between actions and effects?
a. Frame-based reasoning
b. Conceptual dependency
c. Neural networks
d. Rule-based systems
13. Alpha-Beta pruning can be best applied to which type of game?
a. Single-player games
b. Two-player, turn-based games
c. Random chance games
d. Real-time strategy games
14. What does a filler represent in slot-filler structures?
a. The slot itself
b. The relation between two objects
c. A specific value for an attribute
d. The category of objects
15. What kind of representation is a frame in AI?
a. Symbolic reasoning
b. Sequential processing
c. Procedural representation
d. Structured knowledge representation
16. What type of data structure is primarily used to represent hierarchical relationships in AI?
a. Frame
b. Semantic network
c. Conceptual dependency
d. Expert system
17. What is a real-world application of a semantic network?
a. Knowledge graphs
b. Pathfinding algorithms
c. Genetic algorithms
d. Neural networks
18. What is the "beta" value in Alpha-Beta pruning?
a. The best value the maximizing player can guarantee
b. The best value the minimizing player can guarantee
c. The current depth of the search tree
d. The difference between the best and worst moves
19. What does pruning mean in the context of game-playing algorithms?
a. Ignoring branches that cannot affect the outcome
b. Searching additional nodes
c. Optimizing the next move
d. Increasing the depth of the game tree
20. What is an example of a complex script in AI?
a. Sorting a list
b. Playing chess
c. Going to a restaurant
d. Solving a math equation
21. What type of AI representation is used to describe the relationship between objects?
a. Frame-based systems
b. Semantic networks
c. Conceptual dependency
d. Neural networks
22. What is the goal of the maximizing player in the Minimax algorithm?
a. Maximize their score
b. Minimize the opponent's score
c. Expand the game tree
d. Prune unnecessary nodes
23. Which of the following describes a weak slot structure?
a. Strongly defined relations
b. Contains structured rules
c. Lacks specific constraints on fillers
d. Uses predefined frames
24. Which of the following is an example of a terminal state in game-playing?
a. Mid-game strategy
b. Expanding the game tree
c. Pruning moves
d. End of the game with a win/loss
25. What is a primary advantage of using frames in knowledge representation?
a. Encapsulates knowledge in a structured format
b. Provides probabilistic outcomes
c. Optimizes search algorithms
d. Learns from data
26. What is a real-world example of a script in AI?
a. Solving a puzzle
b. Booking a flight
c. Translating text
d. Diagnosing a disease
27. In the Alpha-Beta pruning algorithm, what happens when a beta value is less than an alpha value?
a. The game continues
b. The player switches strategies
c. The game ends
d. The subtree is pruned
28. Which is an example of a simple script in AI?
a. Solving equations
b. Ordering food
c. Planning a complex journey
d. Simulating a game
29. What is the main purpose of pruning in game algorithms?
a. To increase accuracy
b. To reduce the search space
c. To expand the game tree
d. To generate random moves
30. What type of reasoning do frames represent in AI?
a. Structured reasoning
b. Sequential processing
c. Probabilistic learning
d. Genetic computation
 Answer
c. Reduce the number of nodes evaluated

b. Opponent

d. Final outcome

b. A data structure for representing a stereotyped

situation

a. A predefined sequence of events

a. The best value the maximizing player can guarantee

c. Lack of ability to represent complex logic

a. Represents actions and events in a structured way

b. The value at the terminal node itself

c. Frames are structured data, while semantic nets are
graphical

a. To prevent the game tree from growing too large

b. Conceptual dependency

b. Two-player, turn-based games

c. A specific value for an attribute

d. Structured knowledge representation

b. Semantic network

a. Knowledge graphs

b. The best value the minimizing player can guarantee

a. Ignoring branches that cannot affect the outcome

c. Going to a restaurant

b. Semantic networks

a. Maximize their score

c. Lacks specific constraints on fillers

d. End of the game with a win/loss

a. Encapsulates knowledge in a structured format

b. Booking a flight

d. The subtree is pruned

b. Ordering food

b. To reduce the search space

a. Structured reasoning