Q#1: Adversarial search is used in:
(A) Single-agent problems
(B) Multi-player competitive games
(C) Local search
(D) Genetic algorithms
Answer: (B) Multi-player competitive games
Q#2: A zero-sum game is one in which:
(A) Both players win
(B) One player’s gain is another’s loss
(C) Total score is zero
(D) Both players lose
Answer: (B) One player’s gain is another’s loss
Q#3: In game trees, nodes represent:
(A) Actions only
(B) States of the game
(C) Heuristic values
(D) Path costs
Answer: (B) States of the game
Q#4: In game trees, edges represent:
(A) Actions
(B) States
(C) Heuristics
(D) Scores
Answer: (A) Actions
Q#5: Minimax algorithm is used to:
(A) Find optimal move in deterministic games
(B) Search local maxima
(C) Solve CSP
(D) Random exploration
Answer: (A) Find optimal move in deterministic games
Q#6: Minimax assumes:
(A) Opponent plays optimally
(B) Opponent plays randomly
(C) No opponent
(D) BFS tree
Answer: (A) Opponent plays optimally
Q#7: In Minimax, a maximizing player:
(A) Chooses moves to maximize score
(B) Chooses moves to minimize score
(C) Plays randomly
(D) Chooses first action
Answer: (A) Chooses moves to maximize score
Q#8: In Minimax, a minimizing player:
(A) Chooses moves to maximize score
(B) Chooses moves to minimize score
(C) Plays randomly
(D) Ignores moves
Answer: (B) Chooses moves to minimize score
Q#9: Terminal nodes in game tree:
(A) Represent end of game
(B) Represent initial state
(C) Are heuristic estimates
(D) Are BFS nodes
Answer: (A) Represent end of game
Q#10: Evaluation function in games:
(A) Assigns value to non-terminal states
(B) Moves randomly
(C) Expands BFS
(D) Ignores states
Answer: (A) Assigns value to non-terminal states
Q#11: A ply in a game tree refers to:
(A) One player’s move
(B) Entire game
(C) BFS level
(D) DFS depth
Answer: (A) One player’s move
Q#12: Depth of search tree is often limited due to:
(A) Memory constraints
(B) Time constraints
(C) Complexity
(D) All of the above
Answer: (D) All of the above
Q#13: Alpha-Beta pruning is used to:
(A) Reduce number of nodes evaluated in Minimax
(B) Expand all nodes
(C) Random search
(D) BFS
Answer: (A) Reduce number of nodes evaluated in Minimax
Q#14: Alpha value in alpha-beta pruning:
(A) Best value for minimizing player
(B) Best value for maximizing player
(C) Random
(D) Goal state
Answer: (B) Best value for maximizing player
Q#15: Beta value in alpha-beta pruning:
(A) Best value for minimizing player
(B) Best value for maximizing player
(C) Random
(D) Initial state
Answer: (A) Best value for minimizing player
Q#16: Alpha-beta pruning guarantees:
(A) Same result as Minimax
(B) Random moves
(C) BFS only
(D) DFS only
Answer: (A) Same result as Minimax
Q#17: Pruning occurs when:
(A) Alpha >= Beta
(B) Alpha < Beta (C) Randomly (D) BFS level > depth
Answer: (A) Alpha >= Beta
Q#18: Game tree complexity depends on:
(A) Branching factor
(B) Depth
(C) Both A & B
(D) None
Answer: (C) Both A & B
Q#19: Branching factor in a game tree refers to:
(A) Number of moves possible from a node
(B) Depth of tree
(C) Heuristic value
(D) Evaluation function
Answer: (A) Number of moves possible from a node
Q#20: Depth-limited search in games is used to:
(A) Handle time/memory constraints
(B) Expand all nodes
(C) Randomly play
(D) Ignore heuristics
Answer: (A) Handle time/memory constraints
Q#21: Expectiminimax is used for:
(A) Deterministic games
(B) Games with chance elements
(C) Local search
(D) Genetic algorithms
Answer: (B) Games with chance elements
Q#22: In stochastic games, chance nodes represent:
(A) Random events like dice rolls
(B) Opponent moves
(C) Player moves
(D) BFS nodes
Answer: (A) Random events like dice rolls
Q#23: Horizon effect occurs when:
(A) Important events occur beyond search depth
(B) Goal is near
(C) BFS completes
(D) DFS depth limited
Answer: (A) Important events occur beyond search depth
Q#24: Iterative deepening in games helps:
(A) Limit search depth gradually
(B) Explore BFS
(C) Ignore heuristics
(D) Random moves
Answer: (A) Limit search depth gradually
Q#25: Evaluation functions should be:
(A) Fast and accurate
(B) Random
(C) Only for terminal nodes
(D) Ignored
Answer: (A) Fast and accurate
Q#26: Quiescence search improves:
(A) Evaluation accuracy in volatile positions
(B) BFS efficiency
(C) DFS depth
(D) Memory usage
Answer: (A) Evaluation accuracy in volatile positions
Q#27: Iterative deepening alpha-beta combines:
(A) Depth limit + alpha-beta pruning
(B) BFS + DFS
(C) Random search
(D) Genetic algorithms
Answer: (A) Depth limit + alpha-beta pruning
Q#28: Minimax tree is complete if:
(A) Search depth finite and all nodes expanded
(B) Depth unlimited
(C) Heuristics used
(D) Random nodes pruned
Answer: (A) Search depth finite and all nodes expanded
Q#29: In chess, evaluation functions consider:
(A) Material advantage
(B) Piece position
(C) Control of center
(D) All of the above
Answer: (D) All of the above
Q#30: In two-player games, adversarial search:
(A) Maximizes own gain while minimizing opponent’s gain
(B) Ignores opponent
(C) Random moves
(D) BFS only
Answer: (A) Maximizes own gain while minimizing opponent’s gain
Q#31: In games with imperfect information, adversarial search:
(A) Uses heuristics and probability
(B) Explores full BFS
(C) Uses DFS only
(D) Ignores uncertainty
Answer: (A) Uses heuristics and probability
Q#32: Utility function in games maps:
(A) States to numeric values
(B) Moves to BFS nodes
(C) Depth to branching factor
(D) None
Answer: (A) States to numeric values
Q#33: Pruning reduces:
(A) Memory usage
(B) Nodes evaluated
(C) Computation time
(D) All of the above
Answer: (D) All of the above
Q#34: Transposition tables are used to:
(A) Avoid re-evaluating same states
(B) Store heuristics only
(C) Expand BFS
(D) DFS only
Answer: (A) Avoid re-evaluating same states
Q#35: Killer heuristic stores:
(A) Good moves from previous positions
(B) BFS nodes
(C) DFS depth
(D) Random states
Answer: (A) Good moves from previous positions
Q#36: Minimax complexity is:
(A) O(b^d)
(B) O(d)
(C) O(b*d)
(D) O(1)
Answer: (A) O(b^d)
Q#37: Alpha-beta pruning complexity can reduce to:
(A) O(b^(d/2))
(B) O(b^d)
(C) O(d)
(D) O(1)
Answer: (A) O(b^(d/2))
Q#38: In real-time games, adversarial search often uses:
(A) Limited depth + heuristics
(B) Full Minimax
(C) BFS only
(D) DFS only
Answer: (A) Limited depth + heuristics
Q#39: In games like backgammon, search includes:
(A) Chance nodes
(B) Minimax nodes
(C) Both A & B
(D) None
Answer: (C) Both A & B
Q#40: Move ordering in alpha-beta improves:
(A) Pruning efficiency
(B) BFS expansion
(C) DFS depth
(D) Memory usage
Answer: (A) Pruning efficiency
Q#41: Iterative deepening ensures:
(A) Optimal move within time limit
(B) Random moves
(C) BFS only
(D) DFS only
Answer: (A) Optimal move within time limit
Q#42: Quiescence search extends search in:
(A) Volatile positions
(B) Stable positions
(C) BFS only
(D) DFS only
Answer: (A) Volatile positions
Q#43: Evaluation function in non-terminal nodes helps:
(A) Estimate position strength
(B) Store memory
(C) Expand BFS
(D) DFS only
Answer: (A) Estimate position strength
Q#44: Minimax with depth cutoff uses:
(A) Evaluation function at cutoff
(B) BFS only
(C) DFS only
(D) Random moves
Answer: (A) Evaluation function at cutoff
Q#45: Alpha-beta pruning cannot affect:
(A) Final Minimax result
(B) Nodes evaluated
(C) Computation time
(D) Memory usage
Answer: (A) Final Minimax result
Q#46: Adversarial search is essential in:
(A) Chess
(B) Tic-Tac-Toe
(C) Checkers
(D) All of the above
Answer: (D) All of the above
Q#47: Depth-first Minimax explores:
(A) Deepest branches first
(B) Shallow nodes first
(C) Random nodes
(D) BFS nodes
Answer: (A) Deepest branches first
Q#48: Heuristic evaluation should be:
(A) Fast to compute
(B) Accurate
(C) Both A & B
(D) None
Answer: (C) Both A & B
Q#49: In two-player games, a tie results in:
(A) Zero utility for both
(B) Max utility
(C) Min utility
(D) Random utility
Answer: (A) Zero utility for both
Q#50: The main goal of adversarial search is to:
(A) Choose optimal moves considering opponent
(B) Ignore opponent
(C) Random moves
(D) BFS expansion
Answer: (A) Choose optimal moves considering opponent