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