SOLUTION MANUAL
All Chapters Included
,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
, 4 Exercises 1 Introduction
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
© 2023 Pearson Education, Hoboken, NJ. All rights reserved.
All Chapters Included
,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
, 4 Exercises 1 Introduction
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
© 2023 Pearson Education, Hoboken, NJ. All rights reserved.
