Q#1: An agent is anything that:
(A) Stores data
(B) Perceives through sensors and acts through actuators
(C) Compiles programs
(D) Manages memory
Answer: (B) Perceives through sensors and acts through actuators
Q#2: In AI, sensors are used to:
(A) Act on environment
(B) Perceive environment
(C) Store knowledge
(D) Execute code
Answer: (B) Perceive environment
Q#3: Actuators are used by an agent to:
(A) Sense temperature
(B) Perceive signals
(C) Act upon environment
(D) Analyze data
Answer: (C) Act upon environment
Q#4: A rational agent chooses actions that:
(A) Are random
(B) Maximize performance measure
(C) Minimize perception
(D) Ignore environment
Answer: (B) Maximize performance measure
Q#5: The performance measure evaluates:
(A) Agent success
(B) Hardware speed
(C) Memory size
(D) Screen resolution
Answer: (A) Agent success
Q#6: A percept is:
(A) An action taken
(B) Agent’s memory
(C) Agent’s sensory input
(D) Output signal
Answer: (C) Agent’s sensory input
Q#7: A percept sequence is:
(A) Single perception
(B) Complete history of percepts
(C) Future actions
(D) Program code
Answer: (B) Complete history of percepts
Q#8: An environment is fully observable if:
(A) Agent sees partial state
(B) Agent sees complete state
(C) Agent sees nothing
(D) Agent guesses
Answer: (B) Agent sees complete state
Q#9: In a deterministic environment:
(A) Results are unpredictable
(B) Results are certain
(C) Results are random
(D) No results
Answer: (B) Results are certain
Q#10: A stochastic environment involves:
(A) Certainty
(B) Randomness
(C) Stability
(D) Simplicity
Answer: (B) Randomness
Q#11: An episodic environment means:
(A) Each episode is independent
(B) Episodes are dependent
(C) No episodes
(D) Continuous memory
Answer: (A) Each episode is independent
Q#12: A sequential environment means:
(A) Each action independent
(B) Current actions affect future states
(C) No change
(D) Static
Answer: (B) Current actions affect future states
Q#13: A static environment does not:
(A) Change while agent acts
(B) Allow perception
(C) Have states
(D) Support sensors
Answer: (A) Change while agent acts
Q#14: A dynamic environment:
(A) Remains constant
(B) Changes during agent’s decision
(C) Has no states
(D) Is deterministic
Answer: (B) Changes during agent’s decision
Q#15: A discrete environment has:
(A) Infinite states
(B) Continuous values
(C) Finite states/actions
(D) No actions
Answer: (C) Finite states/actions
Q#16: A continuous environment has:
(A) Limited states
(B) Infinite states
(C) No perception
(D) Static states
Answer: (B) Infinite states
Q#17: A simple reflex agent selects action based on:
(A) Current percept only
(B) Past history
(C) Learning
(D) Random choice
Answer: (A) Current percept only
Q#18: A model-based agent keeps track of:
(A) Performance measure
(B) Internal state
(C) Hardware
(D) Compiler
Answer: (B) Internal state
Q#19: A goal-based agent acts to achieve:
(A) Random outputs
(B) Predefined goals
(C) Sensor input
(D) Memory storage
Answer: (B) Predefined goals
Q#20: A utility-based agent aims to:
(A) Maximize utility
(B) Minimize memory
(C) Avoid goals
(D) Remove sensors
Answer: (A) Maximize utility
Q#21: A learning agent has how many main components?
(A) 2
(B) 3
(C) 4
(D) 5
Answer: (C) 4
Q#22: The learning element is responsible for:
(A) Acting
(B) Improving performance
(C) Perceiving
(D) Storing files
Answer: (B) Improving performance
Q#23: The critic provides:
(A) Performance feedback
(B) Code execution
(C) Hardware repair
(D) Random data
Answer: (A) Performance feedback
Q#24: The problem generator suggests:
(A) Known actions
(B) Exploratory actions
(C) No actions
(D) Fixed actions
Answer: (B) Exploratory actions
Q#25: The agent program runs on:
(A) Agent architecture
(B) CPU only
(C) Compiler
(D) OS
Answer: (A) Agent architecture
Q#26: PEAS stands for:
(A) Performance, Environment, Actuators, Sensors
(B) Program, Execution, Action, System
(C) Process, Environment, Agent, State
(D) None of these
Answer: (A) Performance, Environment, Actuators, Sensors
Q#27: Which is an example of an intelligent agent?
(A) Thermostat
(B) Calculator
(C) Printer
(D) Keyboard
Answer: (A) Thermostat
Q#28: Rationality depends on:
(A) Performance measure
(B) Agent’s knowledge
(C) Percept sequence
(D) All of the above
Answer: (D) All of the above
Q#29: Autonomy means agent:
(A) Depends on human
(B) Acts independently
(C) Has no sensors
(D) Is static
Answer: (B) Acts independently
Q#30: A table-driven agent stores:
(A) One action
(B) All percept-action pairs
(C) Only goals
(D) Utilities
Answer: (B) All percept-action pairs
Q#31: Reflex agents work best in:
(A) Fully observable environments
(B) Complex environments
(C) Partial environments
(D) Dynamic world
Answer: (A) Fully observable environments
Q#32: Goal-based agents use:
(A) Search and planning
(B) Random actions
(C) Only sensors
(D) None
Answer: (A) Search and planning
Q#33: Utility function measures:
(A) Happiness level
(B) Desirability of states
(C) Sensor value
(D) Speed
Answer: (B) Desirability of states
Q#34: Learning agents can:
(A) Adapt over time
(B) Stay constant
(C) Remove goals
(D) Ignore feedback
Answer: (A) Adapt over time
Q#35: Which agent type is most flexible?
(A) Simple reflex
(B) Model-based
(C) Goal-based
(D) Learning agent
Answer: (D) Learning agent
Q#36: In partially observable environments, agents must:
(A) Ignore uncertainty
(B) Maintain internal state
(C) Act randomly
(D) Stop working
Answer: (B) Maintain internal state
Q#37: Multi-agent environment involves:
(A) Single agent
(B) Multiple interacting agents
(C) No agent
(D) Static environment
Answer: (B) Multiple interacting agents
Q#38: Competitive environment is:
(A) Cooperative
(B) Adversarial
(C) Static
(D) Deterministic
Answer: (B) Adversarial
Q#39: Cooperative environment involves:
(A) Conflict
(B) Collaboration
(C) Randomness
(D) Isolation
Answer: (B) Collaboration
Q#40: Rational agent does the “right thing” based on:
(A) Luck
(B) Available information
(C) Guess
(D) Random choice
Answer: (B) Available information
Q#41: The mapping from percept sequence to action is called:
(A) Agent function
(B) Utility
(C) Goal
(D) Sensor
Answer: (A) Agent function
Q#42: Environment type for chess is:
(A) Deterministic
(B) Fully observable
(C) Sequential
(D) All of the above
Answer: (D) All of the above
Q#43: A vacuum-cleaner world is example of:
(A) Simple AI environment
(B) Complex robotics
(C) NLP
(D) Vision system
Answer: (A) Simple AI environment
Q#44: The architecture provides:
(A) Hardware platform
(B) Goals
(C) Utility
(D) Feedback
Answer: (A) Hardware platform
Q#45: The agent program implements:
(A) Agent function
(B) Performance
(C) Sensors
(D) Actuators
Answer: (A) Agent function
Q#46: Which factor does NOT affect rationality?
(A) Performance measure
(B) Prior knowledge
(C) Available actions
(D) Screen size
Answer: (D) Screen size
Q#47: An agent in taxi driving domain must consider:
(A) Traffic
(B) Road rules
(C) Passengers
(D) All of the above
Answer: (D) All of the above
Q#48: If environment changes unpredictably, it is:
(A) Static
(B) Deterministic
(C) Stochastic
(D) Simple
Answer: (C) Stochastic
Q#49: A reflex agent with state is also called:
(A) Model-based reflex agent
(B) Utility agent
(C) Learning agent
(D) Random agent
Answer: (A) Model-based reflex agent
Q#50: The ultimate goal of intelligent agents is to:
(A) Maximize rational behavior
(B) Store data
(C) Reduce memory
(D) Avoid actions
Answer: (A) Maximize rational behavior