A A A
Artificial Intelligence: A Modern Approach, 4th Edition
A A A A A A
Mby Peter Norvig and Stuart Russell, Chapters 1 – 28
A A A A A A A A A
ED
ST
U
D
Y
,MEDSTUDY.COM
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 ...
M
6 Constraint Satisfaction Problems ...
ED
III Knowledge, reasoning, and planning
7 Logical Agents ...
8 First-Order Logic ...
ST
9 Inference in First-Order Logic ...
10 Knowledge Representation ...
U
11 Automated Planning ...
IV Uncertain knowledge and reasoning
D
12 Quantifying Uncertainty ...
Y
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 ...
M
26 Robotics ...
VII Conclusions
ED
27 Philosophy, Ethics, and Safety of AI ...
28 The Future of AI
ST
U
D
Y
,MEDSTUDY.COM
EXERCISES A
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?
ExerciseA1.1.#DEFA
DefineAinAyourAownAwords:A (a)Aintelligence,A(b)AartificialAintelligence,A(c)Aagent,A(d)Ara-
M
Ationality,A(e)AlogicalAreasoning.
a. Dictionary definitions of intelligence talk about “the capacity to acquire and apply
ED
knowledge” or “the faculty of thought and reason” or “the ability to comprehend a
nd profit from experience.” These are all reasonable answers, but if we want somet
hing 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 t
ST
hat perform well in a given class of environments, for a given agent architecture; th
ey do the right thing. An important part of that is dealing with the uncertainty of w
hat 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 en
vi- ronment.
U
d. We define rationality as the property of a system which does the “right thing” giv
en what it knows. See Section 2.2 for a more complete discussion. The basic conc
ept is perfect rationality; Section ?? describes the impossibility of achieving perfect
D
rational- ity and proposes an alternative definition.
e. We define logical reasoning as the a process of deriving new sentences from old, su
ch that the new sentences are necessarily true if the old ones are true. (Notice that do
Y
es not refer to any specific syntax or formal language, but it does require a well-
defined notion of truth.)
ExerciseA1.1.#TURI
ReadATuring’sAoriginalApaperAonAAIA(Turing,A1950).A InAtheApaper,AheAdiscussesAseveralAo
bjectionsAtoAhisAproposedAenterpriseAandAhisAtestAforAintelligence.AWhichAobjectionsAstillAcarry
© 2023 Pearson Education, Hoboken, NJ. All rights reserve
Artificial Intelligence: A Modern Approach, 4th Edition
A A A A A A
Mby Peter Norvig and Stuart Russell, Chapters 1 – 28
A A A A A A A A A
ED
ST
U
D
Y
,MEDSTUDY.COM
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 ...
M
6 Constraint Satisfaction Problems ...
ED
III Knowledge, reasoning, and planning
7 Logical Agents ...
8 First-Order Logic ...
ST
9 Inference in First-Order Logic ...
10 Knowledge Representation ...
U
11 Automated Planning ...
IV Uncertain knowledge and reasoning
D
12 Quantifying Uncertainty ...
Y
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 ...
M
26 Robotics ...
VII Conclusions
ED
27 Philosophy, Ethics, and Safety of AI ...
28 The Future of AI
ST
U
D
Y
,MEDSTUDY.COM
EXERCISES A
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?
ExerciseA1.1.#DEFA
DefineAinAyourAownAwords:A (a)Aintelligence,A(b)AartificialAintelligence,A(c)Aagent,A(d)Ara-
M
Ationality,A(e)AlogicalAreasoning.
a. Dictionary definitions of intelligence talk about “the capacity to acquire and apply
ED
knowledge” or “the faculty of thought and reason” or “the ability to comprehend a
nd profit from experience.” These are all reasonable answers, but if we want somet
hing 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 t
ST
hat perform well in a given class of environments, for a given agent architecture; th
ey do the right thing. An important part of that is dealing with the uncertainty of w
hat 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 en
vi- ronment.
U
d. We define rationality as the property of a system which does the “right thing” giv
en what it knows. See Section 2.2 for a more complete discussion. The basic conc
ept is perfect rationality; Section ?? describes the impossibility of achieving perfect
D
rational- ity and proposes an alternative definition.
e. We define logical reasoning as the a process of deriving new sentences from old, su
ch that the new sentences are necessarily true if the old ones are true. (Notice that do
Y
es not refer to any specific syntax or formal language, but it does require a well-
defined notion of truth.)
ExerciseA1.1.#TURI
ReadATuring’sAoriginalApaperAonAAIA(Turing,A1950).A InAtheApaper,AheAdiscussesAseveralAo
bjectionsAtoAhisAproposedAenterpriseAandAhisAtestAforAintelligence.AWhichAobjectionsAstillAcarry
© 2023 Pearson Education, Hoboken, NJ. All rights reserve
