WorksheetsAI Agents and Search Algorithms Quiz
Total questions: 50
Worksheet time: 25mins
An AI agent is defined as an entity that:
Learns only from data
Acts upon an environment using sensors and actuators
Solves only numerical problems
Stores information only
Which of the following is an example of a simple reflex agent?
Chess-playing program
Thermostat
Self-driving car
Virtual assistant
The performance measure of an agent determines:
How fast the agent runs
How much memory it uses
How successful the agent is
The agent’s hardware
A rational agent always:
Acts randomly
Chooses the action that maximizes expected performance
Copies human behavior
Acts emotionally
Which agent keeps track of the world using an internal state?
Simple reflex agent
Learning agent
Model-based agent
Utility-based agent
Sensors are used by agents to:
Perform actions
Measure performance
Perceive the environment
Store data
Actuators are used by agents to:
Sense temperature
Store knowledge
Take actions
Learn models
Which agent improves performance over time?
Simple reflex
Learning agent
Goal-based
Utility-based
PEAS stands for:
Performance, Environment, Actuators, Sensors
Program, Environment, Agent, Sensors
Performance, Evaluation, Actions, States
Planning, Execution, Actions, Sensors
Which agent uses a utility function?
Simple reflex
Model-based
Utility-based
Learning
BFS explores nodes in:
Depth-wise order
Random order
Level-wise order
Heuristic order
DFS uses which data structure?
Queue
Stack
Priority Queue
Tree
BFS is guaranteed to find the shortest path if:
Graph is weighted
Graph is unweighted
Graph has cycles
Graph is infinite
DFS may fail in infinite-depth spaces because:
It uses more memory
It explores shallow nodes first
It can get stuck in infinite paths
It is too slow
BFS memory requirement is:
Low
Moderate
Very high
Zero
DFS is preferred when:
Optimal solution is required
Memory is limited
Path cost matters
Heuristic is available
BFS is complete if:
Branching factor is infinite
Graph is finite
Heuristic is admissible
Path cost is zero
DFS is not optimal because:
It uses stack
It ignores path cost
It finds deeper solutions first
It explores level-wise
BFS time complexity depends on:
Depth only
Branching factor and depth
Heuristic value
Memory size
DFS explores:
All nodes at one level
One branch fully before backtracking
Nodes with lowest cost
Nodes randomly
A* algorithm uses:
Only path cost
Only heuristic
f(n) = g(n) + h(n)
Random cost
g(n) represents:
Estimated cost to goal
Total cost
Cost from start to node n
Heuristic value
h(n) represents:
Cost from start
Estimated cost to goal
Actual cost
Total cost
A heuristic is called admissible if it:
Overestimates cost
Underestimates cost
Is always zero
Is random
A* is optimal if:
h(n) = 0
h(n) is admissible
Graph is infinite
DFS is used
Informed search differs from uninformed search by using:
Sensors
Actuators
Heuristic information
Random moves
Which is an informed search algorithm?
BFS
DFS
A*
Depth-limited search
Greedy Best-First Search uses:
g(n) only
h(n) only
g(n) + h(n)
Random value
A* combines advantages of:
BFS and DFS
UCS and Greedy search
DFS and Greedy
BFS and IDS
If h(n) = 0 for all nodes, A* behaves like:
DFS
BFS
Uniform Cost Search
Greedy Search
Informed search generally:
Explores more nodes
Explores fewer nodes
Uses no memory
Ignores goal
Heuristic function improves search by:
Increasing branching factor
Reducing search space
Increasing memory
Randomizing search
A consistent heuristic satisfies:
h(n) ≤ cost(n,n') + h(n')
h(n) > actual cost
h(n) = 0
h(n) is random
Priority queue is used in:
DFS
BFS
A*
Depth-limited search
A* search may fail if:
Heuristic is admissible
Heuristic overestimates cost
Graph is finite
Cost is positive
An environment is fully observable if:
Agent sees part of the state
Agent sees the complete state
State is hidden
Environment is random
Chess is an example of:
Stochastic environment
Partially observable
Deterministic environment
Continuous environment
A stochastic environment is one where:
Outcome is predictable
Outcome is uncertain
Actions are fixed
Agent is passive
Self-driving car operates in:
Simple environment
Static environment
Dynamic environment
Discrete environment
Taxi driving problem is:
Episodic
Sequential
Static
Single-agent
Medical diagnosis is an example of AI application in:
Education
Robotics
Healthcare
Gaming
Expert systems are mainly used for:
Data storage
Decision making
Image processing
Hardware control
NLP is primarily concerned with:
Images
Numbers
Human language
Robotics
Recommendation systems are used by:
Operating systems
Search algorithms
E-commerce platforms
Compilers
Robotics combines AI with:
Philosophy
Mechanical engineering
Economics
Literature
AI in education helps in:
Manual grading
Personalized learning
Hardware design
Networking
Virtual assistants are examples of:
Simple agents
Intelligent agents
Static agents
Offline systems
AI applications in banking include:
Manual auditing
Fraud detection
Paper filing
Cash handling
Game playing AI mainly uses:
Search algorithms
Sorting algorithms
Hashing
Encryption
The main goal of AI is to:
Replace humans
Make machines intelligent
Store large data
Control hardware
