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ARTIFICIAL INTELLIGENCE FINAL
EXAMINATION: STUDY GUIDE | LATEST
UPDATE 2026/2027 | PRACTICE QUESTIONS
AND ANSWERS | EXAM REVIEW.
TABLE OF CONTENTS
I. Intelligent Agents and Environments
II. Uninformed Search
III. Informed Search and Heuristics
IV. Adversarial Search and Games
V. Constraint Satisfaction Problems
VI. Propositional and First-Order Logic
VII. Planning
VIII. Probabilistic Reasoning and Bayesian Networks
IX. Markov Models, HMMs, and MDPs
X. Machine Learning and Neural Networks
XI. AI Ethics and Professional Responsibility
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question 1: What is the primary function of an intelligent agent?
Correct Answer: To perceive its environment through sensors and act upon that
environment through actuators to achieve its goals.
An intelligent agent maps percept sequences to actions using a policy or agent function. It
operates autonomously and rationally to maximize expected performance. The agent-
environment interaction is the central model in AI.
question 2: What is the difference between a rational agent and an omniscient agent?
Correct Answer: A rational agent maximizes expected performance based on available
information, while an omniscient agent knows the actual outcome of its actions.
Omniscience is impossible in most real environments because the future is uncertain.
Rationality is about making the best decision given current knowledge and probabilities.
Rationality does not require perfect information.
question 3: What does the PEAS framework stand for in agent design?
Correct Answer: Performance measure, Environment, Actuators, Sensors.
PEAS defines the task environment for an agent. For a self-driving car, performance may
include safety and speed, environment includes roads and traffic, actuators include steering
and brakes, and sensors include cameras and LIDAR. PEAS ensures complete specification
of the agent’s task.
question 4: What is the difference between a fully observable and a partially observable
environment?
Correct Answer: In a fully observable environment, the agent can sense the complete
state at each point; in a partially observable environment, some state information is
hidden.
Chess is fully observable; poker is partially observable. Partial observability requires the
agent to maintain a belief state. This distinction affects agent design and search complexity.
question 5: What is a deterministic environment?
Correct Answer: An environment in which the next state is completely determined by
the current state and the agent’s action.
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In deterministic environments, there is no uncertainty about action outcomes. In stochastic
environments, outcomes are probabilistic. Deterministic environments are easier to plan in
but are less realistic for many applications.
question 6: What is a static environment?
Correct Answer: An environment that does not change while the agent is deliberating.
In a static environment, the agent can take time to think without the world changing. In a
dynamic environment, the world can change while the agent decides. Static environments are
simpler but less common in real-world robotics.
question 7: What is a discrete environment?
Correct Answer: An environment with a finite number of distinct states, actions, and
percepts.
Chess is discrete; driving is continuous. Discrete environments allow enumeration and exact
algorithms. Continuous environments require function approximation and calculus-based
methods.
question 8: What is an episodic environment?
Correct Answer: An environment where the agent’s experience is divided into
independent episodes, and the quality of an action depends only on the current episode.
In episodic environments, the agent does not need to think ahead about future episodes. In
sequential environments, current actions affect all future decisions. Most real-world tasks are
sequential.
question 9: What is the agent function?
Correct Answer: A mapping from percept sequences to actions that defines the agent’s
behavior.
The agent function is the abstract specification of the agent. The agent program is the
concrete implementation. Two different programs can implement the same agent function.
question 10: What is the difference between a simple reflex agent and a model-based agent?
ARTIFICIAL INTELLIGENCE FINAL
EXAMINATION: STUDY GUIDE | LATEST
UPDATE 2026/2027 | PRACTICE QUESTIONS
AND ANSWERS | EXAM REVIEW.
TABLE OF CONTENTS
I. Intelligent Agents and Environments
II. Uninformed Search
III. Informed Search and Heuristics
IV. Adversarial Search and Games
V. Constraint Satisfaction Problems
VI. Propositional and First-Order Logic
VII. Planning
VIII. Probabilistic Reasoning and Bayesian Networks
IX. Markov Models, HMMs, and MDPs
X. Machine Learning and Neural Networks
XI. AI Ethics and Professional Responsibility
,2|Page
question 1: What is the primary function of an intelligent agent?
Correct Answer: To perceive its environment through sensors and act upon that
environment through actuators to achieve its goals.
An intelligent agent maps percept sequences to actions using a policy or agent function. It
operates autonomously and rationally to maximize expected performance. The agent-
environment interaction is the central model in AI.
question 2: What is the difference between a rational agent and an omniscient agent?
Correct Answer: A rational agent maximizes expected performance based on available
information, while an omniscient agent knows the actual outcome of its actions.
Omniscience is impossible in most real environments because the future is uncertain.
Rationality is about making the best decision given current knowledge and probabilities.
Rationality does not require perfect information.
question 3: What does the PEAS framework stand for in agent design?
Correct Answer: Performance measure, Environment, Actuators, Sensors.
PEAS defines the task environment for an agent. For a self-driving car, performance may
include safety and speed, environment includes roads and traffic, actuators include steering
and brakes, and sensors include cameras and LIDAR. PEAS ensures complete specification
of the agent’s task.
question 4: What is the difference between a fully observable and a partially observable
environment?
Correct Answer: In a fully observable environment, the agent can sense the complete
state at each point; in a partially observable environment, some state information is
hidden.
Chess is fully observable; poker is partially observable. Partial observability requires the
agent to maintain a belief state. This distinction affects agent design and search complexity.
question 5: What is a deterministic environment?
Correct Answer: An environment in which the next state is completely determined by
the current state and the agent’s action.
, 3|Page
In deterministic environments, there is no uncertainty about action outcomes. In stochastic
environments, outcomes are probabilistic. Deterministic environments are easier to plan in
but are less realistic for many applications.
question 6: What is a static environment?
Correct Answer: An environment that does not change while the agent is deliberating.
In a static environment, the agent can take time to think without the world changing. In a
dynamic environment, the world can change while the agent decides. Static environments are
simpler but less common in real-world robotics.
question 7: What is a discrete environment?
Correct Answer: An environment with a finite number of distinct states, actions, and
percepts.
Chess is discrete; driving is continuous. Discrete environments allow enumeration and exact
algorithms. Continuous environments require function approximation and calculus-based
methods.
question 8: What is an episodic environment?
Correct Answer: An environment where the agent’s experience is divided into
independent episodes, and the quality of an action depends only on the current episode.
In episodic environments, the agent does not need to think ahead about future episodes. In
sequential environments, current actions affect all future decisions. Most real-world tasks are
sequential.
question 9: What is the agent function?
Correct Answer: A mapping from percept sequences to actions that defines the agent’s
behavior.
The agent function is the abstract specification of the agent. The agent program is the
concrete implementation. Two different programs can implement the same agent function.
question 10: What is the difference between a simple reflex agent and a model-based agent?
