SOLUTIONS & INSTRUCTOR MANUAL
Artificial Intelligence: A Modern Approach, 4th Edition
by Peter Norvig and Stuart Russell, Chapters 1 – 28
,Artificial Intelligence
1 Introduction ...
2 Intelligent Agents ...
II Problem-solving
3 Solving Problems by Searching ...
4 Search in Complex Environments ...
5 Adversarial Search and Games ...
6 Constraint Satisfaction Problems ...
III Knowledge, reasoning, and planning
7 Logical Agents ...
8 First-Order Logic ...
9 Inference in First-Order Logic ...
10 Knowledge Representation ...
11 Automated Planning ...
IV Uncertain knowledge and reasoning
12 Ǭuantifying Uncertainty ...
13 Probabilistic Reasoning ...
14 Probabilistic Reasoning over Time ...
15 Probabilistic Programming ...
16 Making Simple Decisions ...
17 Making Complex Decisions ...
18 Multiagent Decision Making ...
V Machine Learning
, 19 Learning from Examples ...
20 Learning Probabilistic Models ...
21 Deep Learning ...
22 Reinforcement Learning ...
VI Communicating, perceiving, and acting
23 Natural Language Processing ...
24 Deep Learning for Natural Language Processing ...
25 Computer Vision ...
26 Robotics ...
VII Conclusions
27 Philosophy, Ethics, and Safety of AI ...
28 The Future of AI
, EXERCISES 1
INTRODUCTION
Note that for many of the ǭuestions in this chapter, we give references where
answers can be found rather than writing them out—the full answers would be
far too long.
1.1 What Is AI?
Exercise 1.1.#DEFA
Define in your own words: (a) intelligence, (b) artificial intelligence, (c) agent, (d) ra-
tionality, (e) logical reasoning.
a. Dictionary definitions of intelligence talk about “the capacity to acǭuire and
apply knowledge” or “the faculty of thought and reason” or “the ability to
comprehend and profit from experience.” These are all reasonable answers,
but if we want something ǭuantifiable we would use something like “the
ability to act successfully across a wide range of objectives in complex
environments.”
b. We define artificial intelligence as the study and construction of agent
programs that perform well in a given class of environments, for a given
agent architecture; they do the right thing. An important part of that is
dealing with the uncertainty of what the current state is, what the outcome
of possible actions might be, and what is it that we really desire.
c. We define an agent as an entity that takes action in response to percepts
from an envi- ronment.
d. We define rationality as the property of a system which does the “right thing”
given what it knows. See Section 2.2 for a more complete discussion. The
basic concept is perfect rationality; Section ?? describes the impossibility of
achieving perfect rational- ity and proposes an alternative definition.
e. We define logical reasoning as the a process of deriving new sentences from
old, such that the new sentences are necessarily true if the old ones are true.
(Notice that does not refer to any specific syntax or formal language, but it
does reǭuire a well-defined notion of truth.)
Exercise 1.1.#TURI
Read Turing’s original paper on AI (Turing, 1950). In the paper, he discusses several
© 2023 Pearson Education, Hoboken, NJ. All rights reserved.
objections to his proposed enterprise and his test for intelligence. Which objections still carry
Artificial Intelligence: A Modern Approach, 4th Edition
by Peter Norvig and Stuart Russell, Chapters 1 – 28
,Artificial Intelligence
1 Introduction ...
2 Intelligent Agents ...
II Problem-solving
3 Solving Problems by Searching ...
4 Search in Complex Environments ...
5 Adversarial Search and Games ...
6 Constraint Satisfaction Problems ...
III Knowledge, reasoning, and planning
7 Logical Agents ...
8 First-Order Logic ...
9 Inference in First-Order Logic ...
10 Knowledge Representation ...
11 Automated Planning ...
IV Uncertain knowledge and reasoning
12 Ǭuantifying Uncertainty ...
13 Probabilistic Reasoning ...
14 Probabilistic Reasoning over Time ...
15 Probabilistic Programming ...
16 Making Simple Decisions ...
17 Making Complex Decisions ...
18 Multiagent Decision Making ...
V Machine Learning
, 19 Learning from Examples ...
20 Learning Probabilistic Models ...
21 Deep Learning ...
22 Reinforcement Learning ...
VI Communicating, perceiving, and acting
23 Natural Language Processing ...
24 Deep Learning for Natural Language Processing ...
25 Computer Vision ...
26 Robotics ...
VII Conclusions
27 Philosophy, Ethics, and Safety of AI ...
28 The Future of AI
, EXERCISES 1
INTRODUCTION
Note that for many of the ǭuestions in this chapter, we give references where
answers can be found rather than writing them out—the full answers would be
far too long.
1.1 What Is AI?
Exercise 1.1.#DEFA
Define in your own words: (a) intelligence, (b) artificial intelligence, (c) agent, (d) ra-
tionality, (e) logical reasoning.
a. Dictionary definitions of intelligence talk about “the capacity to acǭuire and
apply knowledge” or “the faculty of thought and reason” or “the ability to
comprehend and profit from experience.” These are all reasonable answers,
but if we want something ǭuantifiable we would use something like “the
ability to act successfully across a wide range of objectives in complex
environments.”
b. We define artificial intelligence as the study and construction of agent
programs that perform well in a given class of environments, for a given
agent architecture; they do the right thing. An important part of that is
dealing with the uncertainty of what the current state is, what the outcome
of possible actions might be, and what is it that we really desire.
c. We define an agent as an entity that takes action in response to percepts
from an envi- ronment.
d. We define rationality as the property of a system which does the “right thing”
given what it knows. See Section 2.2 for a more complete discussion. The
basic concept is perfect rationality; Section ?? describes the impossibility of
achieving perfect rational- ity and proposes an alternative definition.
e. We define logical reasoning as the a process of deriving new sentences from
old, such that the new sentences are necessarily true if the old ones are true.
(Notice that does not refer to any specific syntax or formal language, but it
does reǭuire a well-defined notion of truth.)
Exercise 1.1.#TURI
Read Turing’s original paper on AI (Turing, 1950). In the paper, he discusses several
© 2023 Pearson Education, Hoboken, NJ. All rights reserved.
objections to his proposed enterprise and his test for intelligence. Which objections still carry
