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Artificial Intelligence (AI) MCQs - Unit-2
1. In adversarial search, the term "minimax" refers to:
A . Maximizing the minimum gain
B . Minimizing the maximum loss
C . Maximizing the maximum gain
D . Minimizing the minimum loss
2. Alpha-Beta pruning is used to:
A . Increase the depth of the search tree
B . Reduce the number of nodes evaluated in the minimax algorithm
C . Increase the branching factor
D . Solve constraint satisfaction problems
3. In a game tree, the optimal decision is found by:
A . Breadth-first search
B . Depth-first search
C . Minimax algorithm
D . Greedy search
4. The primary purpose of Alpha-Beta pruning is to:
A . Improve the heuristic function
B . Reduce the computation time in adversarial search
C . Increase the accuracy of the evaluation function
D . Solve CSPs
5. In Alpha-Beta pruning, alpha represents:
A . The best value for the maximizing player
B . The best value for the minimizing player
C . The worst value for the maximizing player
D . The worst value for the minimizing player
6. Which of the following is NOT a characteristic of adversarial search?
A . Two players with opposing goals
B . Perfect information
C . Randomness in outcomes
D . Zero-sum game
7. In the minimax algorithm, the maximizing player aims to:
A . Minimize the opponent`s score
B . Maximize their own score
C . Minimize their own score
D . Maximize the opponent`s score
8. Alpha-Beta pruning is most effective when:
A . The game tree is shallow
B . The game tree is deep and wide
C . The evaluation function is inaccurate
D . The game is non-zero-sum
9. Which of the following is true about Alpha-Beta pruning?
A . It always evaluates all nodes in the game tree
B . It guarantees the same result as the minimax algorithm
C . It increases the branching factor
D . It is only applicable to constraint satisfaction problems
10. In adversarial search, the evaluation function is used to:
A . Determine the utility of terminal states
B . Estimate the desirability of non-terminal states
C . Solve CSPs
D . Perform constraint propagation
11. A constraint satisfaction problem (CSP) is defined by:
A . Variables, domains, and constraints
B . Variables, heuristics, and goals
C . Variables, actions, and rewards
D . Variables, states, and transitions
12. Backtracking search in CSPs is:
A . A depth-first search with constraint propagation
B . A breadth-first search with heuristic evaluation
C . A greedy search with random restarts
D . A local search with simulated annealing
13. Constraint propagation in CSPs is used to:
A . Reduce the search space by enforcing constraints
B . Increase the branching factor
C . Randomize the search process
D . Solve adversarial search problems
14. The arc consistency algorithm is used in:
A . Constraint propagation
B . Alpha-Beta pruning
C . Propositional logic
D . Minimax search
15. Which of the following is NOT a technique for solving CSPs?
A . Backtracking search
B . Local search
C . Alpha-Beta pruning
D . Constraint propagation
16. In CSPs, a solution is:
A . An assignment of values to variables that satisfies all constraints
B . A sequence of actions leading to a goal state
C . A heuristic evaluation of the search space
D . A random assignment of values to variables
17. The minimum remaining values (MRV) heuristic is used in:
A . Variable ordering in CSPs
B . Value ordering in CSPs
C . Constraint propagation
D . Local search
18. Local search for CSPs is:
A . Complete but not optimal
B . Optimal but not complete
C . Neither complete nor optimal
D . Both complete and optimal
19. The structure of a CSP can be represented as:
A . A constraint graph
B . A game tree
C . A decision tree
D . A state-space graph
20. Which of the following is true about CSPs?
A . They are always solved using backtracking
B . They can be solved using local search techniques
C . They are only applicable to adversarial search problems
D . They require a heuristic function for solving
21. A knowledge-based agent uses:
A . Propositional logic to represent knowledge
B . Adversarial search to make decisions
C . CSPs to solve problems
D . Local search to find solutions
22. In propositional logic, a proposition is:
A . A declarative statement that is either true or false
B . A variable that can take any value
C . A constraint that must be satisfied
D . A heuristic function
23. The Wumpus World is an example of:
A . A knowledge-based agent
B . A constraint satisfaction problem
C . An adversarial search problem
D . A local search problem
24. Propositional logic is:
A . A formal system for representing knowledge
B . A search algorithm
C . A heuristic function
D . A constraint propagation technique
25. Proof by resolution is used in:
A . Propositional theorem proving
B . Adversarial search
C . Constraint satisfaction problems
D . Local search
26.Horn clauses are a subset of:
A . Propositional logic
B . First-order logic
C . Constraint satisfaction problems
D . Adversarial search
27. Forward chaining is:
A . A data-driven inference method
B . A goal-driven inference method
C . A constraint propagation technique
D . A local search algorithm
28. Backward chaining is:
A . A goal-driven inference method
B . A data-driven inference method
C . A constraint propagation technique
D . A local search algorithm
29. Effective propositional model checking is used to:
A . Verify the correctness of logical formulas
B . Solve CSPs
C . Perform adversarial search
D . Implement local search
30. Agents based on propositional logic use:
A . Logical inference to make decisions
B . Heuristic functions to evaluate states
C . Constraint propagation to solve problems
D . Local search to find solutions
Fill in the Blanks
31. In adversarial search, the __________ algorithm is used to find the optimal decision.
32. Alpha-Beta pruning improves the efficiency of the __________ algorithm.
33. The value of alpha represents the best value for the __________ player.
34. The value of beta represents the best value for the __________ player.
35. In a zero-sum game, one player`s gain is the other player`s __________.
36. The evaluation function in adversarial search estimates the __________ of a game state.
37. Alpha-Beta pruning eliminates branches that cannot influence the __________ decision.
38. The minimax algorithm assumes that both players play __________.
39. In Alpha-Beta pruning, if alpha >= beta, the branch is __________.
40. Imperfect real-time decisions are made using __________ evaluation functions.
41. A CSP consists of variables, domains, and __________.
42. Backtracking search is a __________ search algorithm for solving CSPs.
43. Constraint propagation reduces the search space by enforcing __________.
44. The __________ heuristic selects the variable with the fewest legal values.
45. The __________ heuristic chooses the value that least constrains future choices.
46. Local search for CSPs is __________ but not complete.
47. The structure of a CSP can be represented as a __________ graph.
48. The arc consistency algorithm ensures that all constraints are __________.
49. In CSPs, a solution is an assignment of values to variables that satisfies all __________.
50. The __________ algorithm is used to solve CSPs using local search.
51. A knowledge-based agent uses __________ to represent knowledge.
52. In propositional logic, a __________ is a declarative statement that is either true or false.
53. The Wumpus World is an example of a __________ agent.
54. Propositional logic is a formal system for representing __________.
55. Proof by __________ is a method used in propositional theorem proving.
56. __________ clauses are a subset of propositional logic with at most one positive literal.
57. __________ chaining is a data-driven inference method.
58. __________ chaining is a goal-driven inference method.
59. Effective propositional model checking verifies the correctness of __________ formulas.
60. Agents based on propositional logic use __________ to make decisions.
Relevant Materials :
☞ Artificial Intelligence (AI) MCQs - Unit-1 - [ Artificial Intelligence ]
☞ Artificial Intelligence (AI) MCQs - Unit-2 - [ Artificial Intelligence ]
☞ Artificial Intelligence (AI) MCQs - Unit-3 - [ Artificial Intelligence ]
☞ Artificial Intelligence (AI) MCQs - Unit-4 - [ Artificial Intelligence ]
☞ Artificial Intelligence (AI) MCQs - Unit-5 - [ Artificial Intelligence ]
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