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WorksheetsIntelligent Agent
Total questions: 30
Worksheet time: 15mins
A home assistant robot works in the kitchen to fetch items. Sometimes it finds the fridge door closed and sometimes open. Which best describes the environment?
Static and deterministic
Dynamic and partially observable
Episodic and fully observable
Continuous but single-agent
An ATM machine agent always gives the same output for the same card PIN input. Which environment property is correct?
Stochastic and multi-agent
Deterministic and fully observable
Episodic and dynamic
Continuous and partially observable
In an airport baggage scanner, the AI must detect objects regardless of size, angle, or orientation. Which type of rationality challenge does this involve?
Handling continuous percepts
Ignoring performance measure
Simple reflex action
Episodic environment
A web search engine crawler scans billions of pages continuously. Which environment property best matches this?
Static and episodic
Dynamic and continuous
Deterministic and discrete
Fully observable and static
A chess-playing AI competing against another AI agent is an example of:
Single-agent, deterministic environment
Multi-agent, competitive environment
Episodic, continuous environment
Stochastic, static environment
A voice recognition system must work with noisy input. This means the environment is:
Fully observable
Partially observable
Static and deterministic
Discrete and episodic
A robot vacuum cleaner working around moving pets faces which environment?
Static, single-agent
Dynamic, stochastic
Deterministic, discrete
Episodic, fully observable
A self-driving taxi avoids a shortcut road because it is under construction. Which factor of rationality influences this decision?
Percept sequence
Performance measure
Limited action set
Ignoring future consequences
A health monitoring AI advises patients differently based on age and medical history. This shows rationality depends on:
Random decision making
Available percepts and model of environment
Fixed reflex rules
Ignoring environment changes
A delivery drone takes a longer route to save battery by avoiding hills. This is rational because:
It reduces path length only
It considers overall performance measure
It ignores percepts
It always repeats actions
Which situation best describes bounded rationality?
A chess AI explores all moves before deciding
A delivery bot uses limited time and memory to pick a near-best route
A weather AI predicts perfectly for the next year
A reflex agent making random choices
A goal-based AI fails to reach its target due to missing percepts. To be more rational, it should:
Add learning elements or model updates
Reduce its goal space
Ignore percept history
Remove utility measures
Which agent type would best suit a fire-fighting robot that must learn new escape routes over time?
Simple reflex agent
Model-based agent
Learning agent
Utility-based agent
A goal-based delivery bot fails when a new road closure is introduced. What is missing in its structure?
Performance measure
Learning element
Actuator
Reflex rules
Which agent type explicitly compares different possible outcomes before acting?
Reflex agent
Goal-based agent
Utility-based agent
Model-based agent
A stock trading bot deciding how much to invest vs. save is an example of:
Reflex agent
Goal-based agent
Utility-based agent
Episodic agent
In a learning agent, which element provides feedback about performance?
Critic
Performance element
Problem generator
Actuator
A navigation AI plans routes using a map and traffic updates. This is an example of:
Problem-solving agent
Reflex agent
Utility-based agent
Episodic agent
In problem-solving, the initial state is:
The agent’s goal test
The starting point before any action
The set of possible actions
The performance measure
In a maze-solving problem, the goal test checks:
If the agent has reached the exit
If the path is shortest
If percepts are complete
If the performance measure is correct
Which statement about problem-solving agents is true?
They always act without considering the future
They construct sequences of actions to reach goals
They ignore percepts completely
They don’t use search
A robot arm arranging blocks to match a target pattern is using:
Percept sequence
Search through state space
Model-based reflex rules
Episodic decision making
A maze-solving robot tries all possible paths level by level. Which strategy does this describe?
Depth First Search
Breadth First Search
Uniform Cost Search
Iterative Deepening
Which search strategy might go deep into one path and fail to return in time?
BFS
DFS
UCS
IDS
A delivery AI uses UCS to minimize distance traveled. What is the priority for node expansion?
Depth of node
Path cost so far
Alphabetical order of states
Random selection
Iterative Deepening Search (IDS) is often used because:
It requires less memory like DFS but remains complete like BFS
It always guarantees the optimal path at zero cost
It expands all nodes at once
It ignores depth limits
Why is BFS not practical for chess AI?
Chess has a very large branching factor
BFS ignores goal states
BFS always falls into loops
Chess is deterministic
A robot vacuum cleaner using DFS in a big house may:
Always clean optimally
Use too much memory
Get stuck in deep paths without solution
Guarantee least-cost cleaning
Which is a trade-off of IDS?
Higher time use but lower memory use
Lower time use but higher memory use
No goal test required
Ignores branching factor
If a GPS AI uses UCS but roads have negative costs, what problem arises?
UCS still finds shortest path
UCS may enter infinite loop
UCS behaves like BFS
UCS guarantees zero-cost path
