Quiz: Artificial Intelligence Question Bank — 21 domande

1. How does an intelligent agent interact with its environment?

A. It perceives through actuators and acts through sensors

B. It evaluates performance through sensors and plans through actuators

C. It perceives through sensors and acts through actuators

D. It stores percepts in sensors and evaluates goals through actuators

Answer: (C)

Spiegazione

An intelligent agent receives information about its environment through sensors and affects the environment through actuators. Sensors provide percepts, whereas actuators perform actions.

2. Which set of components is represented by the PEAS framework?

A. Performance measure, Environment, Actuators, and Sensors

B. Performance measure, Exploration, Actions, and Search

C. Plans, Environment, Actuators, and Solutions

D. Percepts, Evaluation, Actions, and States

Answer: (A)

Spiegazione

PEAS stands for Performance measure, Environment, Actuators, and Sensors. The performance measure evaluates the agent, while sensors provide its percepts.

3. What criterion does a rational agent use when selecting an action?

A. The action that requires the fewest actuators regardless of its outcome

B. The action expected to maximize performance given percepts and knowledge

C. The action that was most recently successful in a similar environment

D. The action that minimizes the number of percepts received by the agent

Answer: (B)

Spiegazione

A rational agent chooses the action expected to maximize its performance measure based on its percept sequence and available knowledge.

4. What distinguishes informed search from uninformed search?

A. Uninformed search can operate only when every action has the same cost

B. Informed search expands nodes without using any problem-specific information

C. Informed search uses additional heuristic knowledge to guide exploration

D. Uninformed search uses a heuristic while informed search ignores extra knowledge

Answer: (C)

Spiegazione

Uninformed search has no problem-specific heuristic guidance, whereas informed search uses additional knowledge to direct exploration.

5. A search procedure always expands the shallowest unexpanded node before considering deeper nodes. Which procedure is this?

A. Greedy Best-First Search

B. Breadth-First Search

C. Depth-First Search

D. Uniform Cost Search

Answer: (B)

Spiegazione

Breadth-First Search expands nodes in order of depth, processing the shallowest unexpanded nodes before deeper ones.

6. What does Depth-First Search do when the current branch cannot lead to a solution?

A. It backtracks and expands another available branch

B. It adds a heuristic estimate to the current branch cost

C. It returns to the root and expands every shallow node first

D. It selects the frontier node with the lowest path cost

Answer: (A)

Spiegazione

Depth-First Search follows the deepest unexpanded node and backtracks when the current branch cannot produce a solution.

7. Which frontier node is expanded next by Uniform Cost Search?

A. The node with the lowest estimated remaining cost

B. The node at the shallowest depth

C. The node on the deepest available branch

D. The node with the lowest accumulated path cost

Answer: (D)

Spiegazione

Uniform Cost Search prioritizes the frontier node whose accumulated path cost is lowest. This differs from depth-based or heuristic-only selection.

8. What does a heuristic function estimate in a search problem?

A. The cost already accumulated along the path to a state

B. The number of actions previously executed by the agent

C. The probability that the current state was generated correctly

D. The cost of reaching a goal from a given state

Answer: (D)

Spiegazione

A heuristic estimates the remaining cost from a given state to a goal and guides heuristic search. Path cost, by contrast, records cost already incurred.

9. How does A* prioritize nodes during search?

A. By using only the estimated remaining cost

B. By combining accumulated path cost with estimated remaining cost

C. By selecting the deepest node and backtracking after failure

D. By expanding the shallowest node regardless of its costs

Answer: (B)

Spiegazione

A* combines the cost accumulated to reach a state with a heuristic estimate of the remaining cost. Greedy best-first search uses only the heuristic estimate.

10. What is the basic operation performed repeatedly by hill climbing?

A. Moving from the current state to a better neighboring state

B. Searching the entire state space before making any move

C. Accepting every neighboring state regardless of its quality

D. Expanding the frontier node with the lowest accumulated path cost

Answer: (A)

Spiegazione

Hill climbing repeatedly moves to a neighboring state that is better according to the optimization objective.

11. Which move does the minimax strategy select in a two-player game?

A. The move with the highest outcome assuming the opponent chooses randomly

B. The move requiring the fewest searches regardless of the opponent’s response

C. The move with the highest outcome assuming the opponent responds optimally against the agent

D. The move that maximizes the agent’s immediate score without considering later turns

Answer: (C)

Spiegazione

Minimax chooses the move that maximizes the best outcome against an opponent assumed to minimize the agent’s outcome. Unlike single-agent search, it explicitly models an opposing player’s adverse choice.

12. What is the purpose of alpha-beta pruning in game-tree search?

A. To assign heuristic values to every possible game state

B. To remove branches that cannot change the final minimax decision

C. To guarantee that the first move examined is the optimal move

D. To replace the opponent’s strategy with a random selection process

Answer: (B)

Spiegazione

Alpha-beta pruning eliminates game-tree branches that cannot affect the final minimax decision, reducing the number of evaluated branches without changing the selected move.

13. Which three components define a constraint satisfaction problem?

A. States, actions, and rewards for reaching a goal

B. Variables, possible domains, and constraints on value combinations

C. Nodes, edges, and path costs in a search graph

D. Facts, rules, and conclusions derived by inference

Answer: (B)

Spiegazione

A CSP consists of variables, the domains of possible values for those variables, and constraints restricting which combinations are allowed.

14. A backtracking solver assigns values to variables and then encounters a violated constraint. What should it do next?

A. Keep the assignment and ignore the violated constraint

B. Return to an earlier assignment and try a different value

C. Restart the entire search without retaining any assignments

D. Delete every value from the remaining variable domains

Answer: (B)

Spiegazione

Backtracking assigns variables incrementally and returns to an earlier assignment when a constraint is violated, allowing an alternative value to be tried.

15. What distinguishes a knowledge-based agent from a simple reflex agent?

A. It selects actions solely from the current percept

B. It avoids representing information about the environment

C. It uses stored facts and rules with inference to choose actions

D. It chooses actions randomly when several options are available

Answer: (C)

Spiegazione

A knowledge-based agent stores representations of facts and rules and reasons over them to select actions. A simple reflex agent relies only on the current percept.

16. Which statement best describes propositional logic?

A. It represents whole propositions as true or false and combines them with connectives

B. It represents uncertainty by assigning probabilities to hypotheses

C. It represents objects and relations using variables and quantified statements

D. It represents actions as paths through a game tree

Answer: (A)

Spiegazione

Propositional logic represents propositions that are either true or false and combines them using logical connectives. Objects, relations, and quantified statements are features of First-Order Logic.

17. Which capability is characteristic of First-Order Logic?

A. Representing only complete propositions with no internal structure

B. Representing objects, relations, functions, predicates, and quantified statements

C. Applying game strategies against an optimizing opponent

D. Assigning probability values to every possible conclusion

Answer: (B)

Spiegazione

First-Order Logic can describe objects, predicates, relations, functions, and quantified statements within a domain, providing more expressive structure than propositional logic.

18. How does forward chaining derive a new conclusion?

A. It repeatedly applies rules whose premises match known facts

B. It selects a move by assuming an opponent minimizes the outcome

C. It assigns probabilities to competing hypotheses using prior beliefs

D. It begins with a goal and searches backward for supporting rules

Answer: (A)

Spiegazione

Forward chaining starts with known facts and repeatedly applies rules whose premises match those facts, producing new conclusions.

19. When does uncertainty arise in artificial intelligence?

A. When an agent has no rules for representing knowledge at all

B. When an agent cannot determine the true state or outcome with complete confidence

C. When every proposition has already been proven true or false

D. When known conditions always produce a fixed and predictable outcome

Answer: (B)

Spiegazione

Uncertainty occurs when the agent lacks complete confidence about the true state or outcome. Deterministic reasoning instead assumes fixed outcomes from known conditions.

20. What does Bayesian inference do with evidence and prior beliefs?

A. It assigns conclusions without representing degrees of belief

B. It updates the probabilities of hypotheses

C. It eliminates the need to represent alternative hypotheses

D. It guarantees that every hypothesis receives the same probability

Answer: (B)

Spiegazione

Bayesian inference updates the probability of hypotheses by combining new evidence with prior beliefs, thereby representing degrees of belief rather than deterministic conclusions.

21. What does a Bayesian network represent?

A. Logical contradictions among propositions in an undirected structure

B. Exact assignments of values to variables in a constraint problem

C. The sequence of moves selected by two competing game players

D. Probabilistic dependencies among variables in a directed graphical structure

Answer: (D)

Spiegazione

A Bayesian network uses a directed graphical structure to represent probabilistic dependencies among variables.