Q#1: What is the first step in problem-solving in AI?
(A) Acting randomly
(B) Defining the problem
(C) Learning from data
(D) Using actuators
Answer: (B) Defining the problem
Q#2: The state space of a problem is:
(A) All possible actions
(B) All possible states
(C) Only the goal state
(D) Only the initial state
Answer: (B) All possible states
Q#3: In AI search, an initial state refers to:
(A) Goal state
(B) Starting point of the problem
(C) Random state
(D) Terminal node
Answer: (B) Starting point of the problem
Q#4: A goal state is:
(A) Any state
(B) Desired solution of the problem
(C) Initial state
(D) Root node
Answer: (B) Desired solution of the problem
Q#5: Actions in search problems are:
(A) Choices available to reach next states
(B) Sensors
(C) Performance measures
(D) Feedback signals
Answer: (A) Choices available to reach next states
Q#6: A path in state space is:
(A) Sequence of states from initial to goal
(B) Single action
(C) Goal alone
(D) Random movement
Answer: (A) Sequence of states from initial to goal
Q#7: Search strategies are classified as:
(A) Informed and Uninformed
(B) Stochastic and Deterministic
(C) Reflexive and Learning
(D) Static and Dynamic
Answer: (A) Informed and Uninformed
Q#8: Uninformed search uses:
(A) Heuristic information
(B) No domain knowledge except problem definition
(C) Probabilities
(D) Learning data
Answer: (B) No domain knowledge except problem definition
Q#9: Which of these is an uninformed search method?
(A) A* Search
(B) Breadth-First Search
(C) Best-First Search
(D) Greedy Search
Answer: (B) Breadth-First Search
Q#10: Breadth-First Search expands nodes:
(A) Depth first
(B) Level by level
(C) Randomly
(D) Using heuristic
Answer: (B) Level by level
Q#11: Depth-First Search expands nodes:
(A) Level by level
(B) Deepest unexpanded node first
(C) Randomly
(D) Using cost
Answer: (B) Deepest unexpanded node first
Q#12: DFS can get stuck in:
(A) Loops
(B) Goal states
(C) Breadth levels
(D) Performance measure
Answer: (A) Loops
Q#13: Uniform-Cost Search is used when:
(A) Actions have same cost
(B) Actions have different costs
(C) Heuristics available
(D) No goal
Answer: (B) Actions have different costs
Q#14: A heuristic function estimates:
(A) Actual cost to reach goal
(B) Number of actions
(C) Sensor accuracy
(D) Initial state
Answer: (A) Actual cost to reach goal
Q#15: Greedy Best-First Search uses:
(A) Path cost only
(B) Heuristic estimate only
(C) Both path cost and heuristic
(D) None
Answer: (B) Heuristic estimate only
Q#16: A* Search combines:
(A) Depth and Breadth
(B) Path cost and heuristic
(C) Sensors and actuators
(D) Random and goal-based search
Answer: (B) Path cost and heuristic
Q#17: Admissible heuristics never:
(A) Underestimate cost
(B) Overestimate cost
(C) Reach goal
(D) Are useful
Answer: (B) Overestimate cost
Q#18: Consistent heuristics satisfy:
(A) Triangle inequality
(B) Linear growth
(C) Random choice
(D) Cost function
Answer: (A) Triangle inequality
Q#19: Iterative Deepening Search combines:
(A) DFS and BFS advantages
(B) Heuristics only
(C) Random and uniform search
(D) Sensors and actuators
Answer: (A) DFS and BFS advantages
Q#20: A state space graph can have:
(A) Loops
(B) Branches
(C) Multiple paths to goal
(D) All of the above
Answer: (D) All of the above
Q#21: A blind search is also called:
(A) Informed search
(B) Uninformed search
(C) Heuristic search
(D) Greedy search
Answer: (B) Uninformed search
Q#22: Which search guarantees the shortest path?
(A) DFS
(B) BFS
(C) Greedy
(D) Random
Answer: (B) BFS
Q#23: DFS is memory efficient because:
(A) Stores all nodes
(B) Stores only current path
(C) Stores goal nodes
(D) Stores heuristic
Answer: (B) Stores only current path
Q#24: Depth-Limited Search avoids:
(A) Loops
(B) Infinite depth
(C) Goal detection
(D) All nodes
Answer: (B) Infinite depth
Q#25: Bidirectional Search searches:
(A) From initial state only
(B) From both initial and goal states
(C) Randomly
(D) Using heuristic
Answer: (B) From both initial and goal states
Q#26: Hill Climbing is a:
(A) Uninformed search
(B) Local search
(C) Breadth-first search
(D) Dynamic programming
Answer: (B) Local search
Q#27: Hill Climbing can get stuck in:
(A) Local maxima
(B) Global maxima
(C) Goal
(D) Initial state
Answer: (A) Local maxima
Q#28: Simulated Annealing helps avoid:
(A) Local maxima
(B) Goal states
(C) Loops
(D) BFS expansion
Answer: (A) Local maxima
Q#29: Genetic Algorithms are based on:
(A) Search trees
(B) Evolution and selection
(C) BFS only
(D) DFS only
Answer: (B) Evolution and selection
Q#30: Constraint Satisfaction Problems (CSP) include:
(A) Variables, Domains, Constraints
(B) Sensors, Actuators
(C) Heuristics only
(D) Random values
Answer: (A) Variables, Domains, Constraints
Q#31: Backtracking is used in:
(A) CSP
(B) Greedy search
(C) A* search
(D) DFS only
Answer: (A) CSP
Q#32: Forward checking helps to:
(A) Detect future conflicts
(B) Ignore constraints
(C) Expand DFS
(D) Evaluate heuristic
Answer: (A) Detect future conflicts
Q#33: Minimax algorithm is used in:
(A) Single-agent search
(B) Two-player games
(C) CSP
(D) Genetic search
Answer: (B) Two-player games
Q#34: Alpha-Beta pruning improves:
(A) Search space efficiency
(B) Heuristic accuracy
(C) BFS memory
(D) DFS depth
Answer: (A) Search space efficiency
Q#35: Depth of a search tree affects:
(A) Time complexity
(B) Space complexity
(C) Both time and space
(D) None
Answer: (C) Both time and space
Q#36: Branching factor refers to:
(A) Number of successors per node
(B) Depth of tree
(C) Goal states
(D) Initial state
Answer: (A) Number of successors per node
Q#37: A* is complete if:
(A) Heuristic is admissible
(B) Heuristic is random
(C) Path cost ignored
(D) DFS applied
Answer: (A) Heuristic is admissible
Q#38: Uniform-cost search is:
(A) Optimal
(B) Incomplete
(C) Random
(D) Heuristic-based
Answer: (A) Optimal
Q#39: Greedy search is:
(A) Complete
(B) Not guaranteed to be optimal
(C) Uses path cost
(D) BFS variant
Answer: (B) Not guaranteed to be optimal
Q#40: Local search algorithms use:
(A) Entire state space
(B) Limited memory
(C) Heuristic information
(D) Both B & C
Answer: (D) Both B & C
Q#41: Beam search limits:
(A) Depth
(B) Width of nodes expanded
(C) Actions
(D) Path length
Answer: (B) Width of nodes expanded
Q#42: Hill climbing variants include:
(A) Stochastic
(B) Random-restart
(C) First-choice
(D) All of the above
Answer: (D) All of the above
Q#43: In search problems, goal test is:
(A) Condition to determine if goal reached
(B) Path cost evaluation
(C) Memory optimization
(D) Action selection
Answer: (A) Condition to determine if goal reached
Q#44: Breadth-first search has time complexity of:
(A) O(b^d)
(B) O(d)
(C) O(b*d)
(D) O(1)
Answer: (A) O(b^d)
Q#45: Depth-first search has space complexity of:
(A) O(b^d)
(B) O(d)
(C) O(bd)
(D) O(1)
Answer: (C) O(bd)
Q#46: Hill climbing evaluates:
(A) Neighboring states
(B) Entire state space
(C) Goal only
(D) Path cost only
Answer: (A) Neighboring states
Q#47: Greedy search evaluates nodes based on:
(A) Path cost
(B) Heuristic value
(C) Random selection
(D) BFS order
Answer: (B) Heuristic value
Q#48: Search algorithms are used in:
(A) Game playing
(B) Pathfinding
(C) Scheduling
(D) All of the above
Answer: (D) All of the above
Q#49: Depth-limited search may fail if:
(A) Solution is beyond depth limit
(B) Heuristic fails
(C) Environment changes
(D) Goal is too simple
Answer: (A) Solution is beyond depth limit
Q#50: In AI, the main goal of search is to:
(A) Find solution efficiently
(B) Use maximum memory
(C) Avoid heuristics
(D) Randomly explore states
Answer: (A) Find solution efficiently