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AI - First Exam

❖ Multiple Choice Questions:

1) Consider “mathematician’s theorem – proving assistant” agent environment, which of

the following is correct?

a) partially observable, deterministic, static, discrete

b) fully observable, deterministic, static, discrete
c) partially observable, stochastic, dynamic, continuous
d) fully observable, stochastic, static, discrete

2) The environment for “robot football player” agent is considered.

a) partially observable, stochastic, static, discrete

b) partially observable, stochastic, dynamic, continuous
c) fully observable, deterministic, static, discrete
d) cannot be determined

3) Consider “playing tic-tac-toe” agent, the environment is:

a) partially observable, deterministic, static, discrete

b) fully observable, stochastic, static, discrete
c) fully observable, deterministic, static, discrete
d) cannot be determined

4) Rational agent always does the right things:

a) True b) False

5) Select the most appropriate situation for that a blind search can be used:

a) Real-life situation c) Complex game.

b) Small Search Space d) All of the above.

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6) A technique that was developed to determine whether a machine could or not could
does artificial intelligence known as the:

a) Boolean Algebra c) Algorithm

b) Turing test d) Knowledge Representation

7) Which of the following is not considered as artificial intelligence application?

a) Expert System d) Vision System

b) Gaming e) Natural language processing
c) All the options are AI applications f) All the options are not AI applications

8) Which agent deals with happy and unhappy state?

a) Utility- based agent c) Goal- based agent

b) Model – based agent d) Learning agent

9) For the following tree, show the order of nodes visited for depth - first search, the goal
node is l and the numbers next to edges indicate the associated cost.

a) ABCDEFGHI c) ABECFGI
b) ABCDFGI d) The goal node can not be reached

10) For the same above tree show the order of nodes visited for breadth-first search, the
goal node is 1 and the numbers next to edges indicate the associated cost.

a) ABCDEFGHI c) ABECFGI
b) ABCDFGI d) The goal node cannot be reached

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11) Consider playing tic-tac-toe agent (X-O game) , the environment is :

a) Fully observable, stochastic, static, discrete.

b) Partially observable, deterministic, static, discrete.
c) Fully observable, deterministic, static, discrete.
d) Cannot be determined.

12) A fully observable environment can be considered partially observable due to errors
and noise in an agent’s sensors.

a) True b) False

13) Economics is considered as an AI foundation.

a) True b) False

14) In the design phase, design the _____ according to what one actually wants in the
environment rather than how one thinks the agent should behave.

a) Performance measures.
b) Environment.
c) Sensors.
d) Actuators.
e) None of the options.

15) Suppose that an agent lives in a grid world of size 5 * 5 (for total of 25 squares). The
agent has two sensors: a GPS sensor, which informs the agent of its current location on the
grid, and a camera sensor, which informs the agent of the color on the current square and
four adjacent squares. The agent, at each step, moves left, right, top, bottom. 24 of the 25
squares are safe, and one square (at location 4,3) is dangerous. The current location of the
agent is safe. If the agent is model based reflex, the safe squares are green, and the
dangerous squares are red, is it possible for this agent to follow a safe strategy that will
always avoid the dangerous squares? if yes, what is the strategy?

a) Yes, the strategy is to never visit a red square.

b) No, model-based reflex cannot be used in this case.
c) Can not be determined.

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16) In a partially observable environment, it is impossible for an agent to act rationally.

a) False b) True

17) An agent’s environment is said to be stochastic if the next state is completely

determined by the current state and the agent’s action.

a) False b) True

18) Suppose that an agent lives in a grid world of size 10 * 10 (for total of 100 squares).
The agent has two sensors: a recognize sensor, which determines whether this is gold or
not, and Receiver of radiation intensity, which determine the intensity of rays coming out
of the gold. The agent can move in any direction. Can the simple reflexive agent find the
gold ?

a) True b) False

19) The main goal of AI is to make systems that think and act like humans:

a) True b) False

20) Alan Turing made his famous Turing test to determine if a system is “Turing
computable”:

a) True b) False

21) AI applications:

a) Games
b) Scheduling and planning
c) Medicine
d) All

22) The agent interacts with environment using:

a) Sensors and actuators

b) Sensors only
c) Actuators only
d) None

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23) Works only if the environment is fully observable:

a) Simple reflexive agent

b) Model based reflexive agent
c) Goal based agent
d) Utility based agent

24) The learning agent __________ component that suggests actions that will lead to new
and informative experience is:

a) Problem generator
b) Performance element
c) Critic
d) Learning element

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❖ Essay Questions:

1) The traveling salesperson problem (TSP) is a touring problem in which each city must
be visited exactly once, the man is to find the shortest tour.

Describe the components of the above problem in the following table.

Initial state

Successor function (actions)

Goal test

Path cost

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2) Imagine “Interactive English Tutor” agent, fill the following table with PEAS
descriptions.

p E A S

3) Suppose the goal state is 11. List the order in which nodes will be visited for depth -
limited search with limit 3.

Answer:

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4) Suppose that an agent lives in a grid world of size 5 * 5 (for total of 25 squares). The
agent has two sensors: a GPS sensor, which informs the agent of its current location on the
grid, and a camera sensor, which informs the agent of the color on the current square and
four adjacent squares. The agent, at each step, moves left, right, top, bottom. 24 of the 25
squares are safe, and one square (at location 4,3) is dangerous. The current location of the
agent is safe.

a) If the agent is model based reflex, the safe squares are green, and the dangerous square is red,
is it possible for this agent to follow a safe strategy that will always avoid the dangerous
square? if yes, what is that strategy?

b) If the agent is model based reflex, and all squares (safe and dangerous) are green, is it
possible for this agent to follow a safe strategy that will always avoid the dangerous square? if
yes, what is that strategy?