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 Quantifying 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 questions 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 acquire 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 quantifiable 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 require 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
objections to his proposed enterprise and his test for intelligence. Which objections still carry
© 2023 Pearson Education, Hoboken, NJ. All rights reserved.
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 Quantifying 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 questions 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 acquire 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 quantifiable 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 require 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
objections to his proposed enterprise and his test for intelligence. Which objections still carry
© 2023 Pearson Education, Hoboken, NJ. All rights reserved.
