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
Notethatformanyofthequestionsinthischapter,wegivereferenceswhereanswerscanbe found
. . . . . . . . . . . . . . . . .
rather than writing them out—the full answers would be far too long.
. . . . . . . . . . . .
1.1 What Is AI?
. . .
Exercise1.1.#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. Wedefinelogicalreasoningastheaprocessofderiving new sentencesfromold,such thatthe
. . . . . . . . . . . . . . . .
new sentences are necessarily true if the old ones are true. (Notice that does not refer to any
. . . . . . . . . . . . . . . . . .
specificsyntaxorformallanguage,butitdoesrequireawell-definednotion of truth.)
. . . . . . . . . . . . . .
Exercise1.1.#TURI .
Read Turing’s original paper on AI (Turing, 1950). In the paper, he discusses several
. . . . . . . . . . . . .
objectionstohisproposedenterpriseandhistestforintelligence. Whichobjectionsstillcarry
. . . . . . . . . . . . . .
© 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
Notethatformanyofthequestionsinthischapter,wegivereferenceswhereanswerscanbe found
. . . . . . . . . . . . . . . . .
rather than writing them out—the full answers would be far too long.
. . . . . . . . . . . .
1.1 What Is AI?
. . .
Exercise1.1.#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. Wedefinelogicalreasoningastheaprocessofderiving new sentencesfromold,such thatthe
. . . . . . . . . . . . . . . .
new sentences are necessarily true if the old ones are true. (Notice that does not refer to any
. . . . . . . . . . . . . . . . . .
specificsyntaxorformallanguage,butitdoesrequireawell-definednotion of truth.)
. . . . . . . . . . . . . .
Exercise1.1.#TURI .
Read Turing’s original paper on AI (Turing, 1950). In the paper, he discusses several
. . . . . . . . . . . . .
objectionstohisproposedenterpriseandhistestforintelligence. Whichobjectionsstillcarry
. . . . . . . . . . . . . .
© 2023 Pearson Education, Hoboken, NJ. All rights reserved.
. . . . . . . .
