Q#1: Beyond classical search, AI focuses on:
(A) Deterministic single-agent problems only
(B) Real-world complex problems
(C) Only puzzles
(D) None of the above
Answer: (B) Real-world complex problems
Q#2: Local search algorithms operate on:
(A) Entire state space
(B) Single current state
(C) All goal states
(D) BFS tree
Answer: (B) Single current state
Q#3: Hill Climbing is a type of:
(A) Uninformed search
(B) Local search
(C) BFS
(D) DFS
Answer: (B) Local search
Q#4: Main problem with hill climbing is:
(A) Expensive memory
(B) Local maxima
(C) Slow computation
(D) No solution
Answer: (B) Local maxima
Q#5: Random-restart hill climbing helps to:
(A) Avoid local maxima
(B) Save memory
(C) Optimize path cost
(D) Explore BFS
Answer: (A) Avoid local maxima
Q#6: Simulated annealing is inspired by:
(A) Evolution
(B) Metallurgy cooling process
(C) Tree search
(D) Heuristic planning
Answer: (B) Metallurgy cooling process
Q#7: Simulated annealing allows:
(A) Only uphill moves
(B) Occasional downhill moves
(C) No moves
(D) Random BFS
Answer: (B) Occasional downhill moves
Q#8: Local beam search maintains:
(A) Single state
(B) k states simultaneously
(C) All states
(D) Random paths
Answer: (B) k states simultaneously
Q#9: Stochastic beam search selects successors:
(A) Deterministically
(B) Probabilistically
(C) Random restart
(D) Heuristic-free
Answer: (B) Probabilistically
Q#10: Genetic algorithms are inspired by:
(A) Natural evolution
(B) Hill climbing
(C) BFS
(D) DFS
Answer: (A) Natural evolution
Q#11: A population in GA consists of:
(A) Single solution
(B) Multiple candidate solutions
(C) Random numbers
(D) Paths only
Answer: (B) Multiple candidate solutions
Q#12: Selection in GA favors:
(A) Random individuals
(B) Fitter individuals
(C) Old individuals
(D) BFS nodes
Answer: (B) Fitter individuals
Q#13: Crossover in GA:
(A) Combines parts of parents
(B) Randomly deletes states
(C) Moves hill climbing nodes
(D) Evaluates heuristics
Answer: (A) Combines parts of parents
Q#14: Mutation in GA helps to:
(A) Avoid local optima
(B) Expand BFS tree
(C) Decrease branching factor
(D) Improve memory
Answer: (A) Avoid local optima
Q#15: In real-world search, the environment is often:
(A) Deterministic
(B) Partially observable
(C) Fully observable
(D) Single-state
Answer: (B) Partially observable
Q#16: Online search algorithms act:
(A) Without knowing full environment
(B) With full environment knowledge
(C) Randomly
(D) Only BFS
Answer: (A) Without knowing full environment
Q#17: Online search can involve:
(A) Learning from experience
(B) Local perception
(C) Replanning
(D) All of the above
Answer: (D) All of the above
Q#18: Real-time search focuses on:
(A) Immediate action selection
(B) BFS expansion
(C) DFS depth
(D) Genetic evolution
Answer: (A) Immediate action selection
Q#19: Memory-bounded search includes:
(A) Depth-limited search
(B) RBFS
(C) Iterative deepening A*
(D) All of the above
Answer: (D) All of the above
Q#20: Recursive Best-First Search (RBFS) is:
(A) Memory-efficient
(B) Uninformed
(C) Greedy only
(D) Random
Answer: (A) Memory-efficient
Q#21: Local search is suitable for:
(A) Large state spaces
(B) Small state spaces
(C) Single-agent deterministic
(D) BFS trees
Answer: (A) Large state spaces
Q#22: Hill climbing evaluates:
(A) Entire state space
(B) Neighboring states only
(C) Goal states
(D) Path cost only
Answer: (B) Neighboring states only
Q#23: Simulated annealing uses:
(A) Deterministic moves
(B) Probabilistic moves
(C) BFS expansion
(D) DFS depth
Answer: (B) Probabilistic moves
Q#24: Local beam search improves:
(A) BFS completeness
(B) Exploration efficiency
(C) Memory usage only
(D) Heuristic admissibility
Answer: (B) Exploration efficiency
Q#25: Genetic algorithms work on:
(A) Single candidate
(B) Population of candidates
(C) BFS nodes
(D) DFS nodes
Answer: (B) Population of candidates
Q#26: Fitness function in GA measures:
(A) State depth
(B) Quality of candidate
(C) BFS width
(D) Randomness
Answer: (B) Quality of candidate
Q#27: GA mutation prevents:
(A) Random moves
(B) Premature convergence
(C) BFS expansion
(D) DFS loops
Answer: (B) Premature convergence
Q#28: Incomplete knowledge requires:
(A) Deterministic search
(B) Online search strategies
(C) Classical BFS
(D) DFS only
Answer: (B) Online search strategies
Q#29: Exploration vs exploitation in local search balances:
(A) BFS vs DFS
(B) Trying new states vs using known good states
(C) Memory vs speed
(D) Heuristic vs path cost
Answer: (B) Trying new states vs using known good states
Q#30: Real-world search may include:
(A) Uncertainty
(B) Multiple agents
(C) Resource constraints
(D) All of the above
Answer: (D) All of the above
Q#31: Online learning differs from offline learning because:
(A) Learns while acting
(B) Uses full model beforehand
(C) Ignores feedback
(D) Uses DFS only
Answer: (A) Learns while acting
Q#32: Anytime algorithms improve:
(A) Solution quality over time
(B) BFS completeness
(C) Memory usage only
(D) Path cost
Answer: (A) Solution quality over time
Q#33: Beam search keeps track of:
(A) Best k states
(B) All states
(C) Single state only
(D) Heuristic nodes
Answer: (A) Best k states
Q#34: Stochastic search algorithms rely on:
(A) Deterministic rules
(B) Randomness
(C) BFS only
(D) DFS only
Answer: (B) Randomness
Q#35: Hill climbing variant “first-choice” selects:
(A) Best neighbor
(B) First better neighbor
(C) Random state
(D) BFS node
Answer: (B) First better neighbor
Q#36: Simulated annealing cooling schedule affects:
(A) Convergence probability
(B) Memory usage
(C) BFS expansion
(D) DFS depth
Answer: (A) Convergence probability
Q#37: Genetic algorithms use:
(A) Selection
(B) Crossover
(C) Mutation
(D) All of the above
Answer: (D) All of the above
Q#38: Multi-agent search can be:
(A) Competitive
(B) Cooperative
(C) Both A & B
(D) Deterministic only
Answer: (C) Both A & B
Q#39: Exploration in search aims to:
(A) Find new states
(B) Repeat old paths
(C) Ignore heuristic
(D) Expand BFS only
Answer: (A) Find new states
Q#40: Exploitation in search aims to:
(A) Use best-known states
(B) Ignore past knowledge
(C) Random moves
(D) DFS only
Answer: (A) Use best-known states
Q#41: Local search algorithms are generally:
(A) Memory efficient
(B) Memory hungry
(C) BFS-based
(D) DFS-based
Answer: (A) Memory efficient
Q#42: Hill climbing with sideways moves can:
(A) Escape plateaus
(B) Avoid global maxima
(C) Improve memory
(D) BFS expansion
Answer: (A) Escape plateaus
Q#43: Genetic algorithm selection methods include:
(A) Roulette wheel
(B) Tournament
(C) Rank-based
(D) All of the above
Answer: (D) All of the above
Q#44: Mutation in GA is usually:
(A) Low probability
(B) High probability
(C) Deterministic
(D) None
Answer: (A) Low probability
Q#45: Online search is useful when:
(A) Environment unknown
(B) Full state known
(C) BFS tree small
(D) DFS tree small
Answer: (A) Environment unknown
Q#46: Local search may get stuck in:
(A) Local maxima
(B) Goal
(C) Initial state
(D) Path cost
Answer: (A) Local maxima
Q#47: Hill climbing without sideways moves can:
(A) Fail on plateaus
(B) Always succeed
(C) BFS only
(D) DFS only
Answer: (A) Fail on plateaus
Q#48: Beam search width controls:
(A) Number of successors kept
(B) Depth of search
(C) BFS levels
(D) DFS nodes
Answer: (A) Number of successors kept
Q#49: Simulated annealing probability of accepting worse states:
(A) Decreases over time
(B) Increases over time
(C) Remains constant
(D) Random
Answer: (A) Decreases over time
Q#50: Beyond classical search, AI focuses on:
(A) Efficient real-world problem solving
(B) Only puzzles
(C) BFS expansion
(D) DFS depth
Answer: (A) Efficient real-world problem solving