Artificial Intelligence A Modern
Approach, 4th Edition
by Stuart Russell
Complete Chapter Solutions Manual
are included (Ch 1 to 28)
** Immediate Download
** Swift Response
** All Chapters included
,Table of Contents are given below
1.Introduction
2.Intelligent Agents
3.Solving Problems by Searching
4.Search in Complex Environments
5.Adversarial Search and Games
6.Constraint Satisfaction Problems
7.Logical Agents
8.First-Order Logic
9.Inference in First-Order Logic
10.Knowledge Representation
11.Automated Planning
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
19.Learning from Examples
20.Learning Probabilistic Models
21.Deep Learning
22.Reinforcement Learning
23.Natural Language Processing
24.Deep Learning for Natural Language Processing
25.Computer Vision
26.Robotics
27.Philosophy and Ethics of AI
28.The Future of AI
,Solutions Manual organized in reverse order, with the last chapter displayed first, to ensure that all
chapters are included in this document. (Complete Chapters included Ch28-1)
EXERCISES 28
THE FUTURE OF AI
[[need exercises]]
28.1 AI Components
Exercise 28.1.#FUTP
Compare the price of various component technologies used in AI, and create charts for
how those prices have changed over the last twenty years.
The answers are changing quickly, and will depend on exactly what month this assignment
is given. Components to compare include CPUs, GPUs, other specialized processors, cloud
computing services, light sensors such as digital cameras, active sensors such as lidar and
radar, and actuators such as robot arms, motors, and chassis.
[[need exercises]]
28.2 AI Architectures
[[need exercises]]
© 2023 Pearson Education, Hoboken, NJ. All rights reserved.
, EXERCISES 27
PHILOSOPHY, ETHICS, AND SAFETY
OF AI
27.1 The Limits of AI
Exercise 27.1.#DISA
Go through Turing’s list of alleged “disabilities” of machines, identifying which have
been achieved, which are achievable in principle by a program, and which are still problem-
atic because they require conscious mental states.
We will take the disabilities (see page 949) one at a time. Note that this exercise might be
better as a class discussion rather than written work.
a. be kind: Certainly there are programs that are polite and helpful, but to be kind requires
an intentional state, so this one is problematic.
b. resourceful: Resourceful means “clever at finding ways of doing things.” Many pro-
grams meet this criteria to some degree: a compiler can be clever making an optimiza-
tion that the programmer might not ever have thought of; a database program might
cleverly create an index to make retrievals faster; a checkers or backgammon program
learns to play as well as any human. One could argue whether the machines are “re-
ally” clever or just seem to be, but most people would agree this requirement has been
achieved.
c. beautiful: Its not clear if Turing meant to be beautiful or to create beauty, nor is it clear
whether he meant physical or inner beauty. Certainly the many industrial artifacts in
the New York Museum of Modern Art, for example, are evidence that a machine can be
beautiful. There are also programs that have created art. One of the best known of these
is chronicled in Aaron’s code: Meta-art, artificial intelligence, and the work of Harold
Cohen (McCorduck, 1991). There have been many instances of computers making art
since, such as with the music of David Cope (Cope, 2008) or the images of Google’s
Deep Dream (Mordvinstev, 2015).
d. friendly This appears to fall under the same category as kind.
e. have initiative Interestingly, there is now a serious debate whether software should take
initiative. The whole field of software agents says that it should; critics such as Ben
Schneiderman say that to achieve predictability, software should only be an assistant,
not an autonomous agent. Notice that the debate over whether software should have
initiative presupposes that it has initiative.
© 2023 Pearson Education, Hoboken, NJ. All rights reserved.
Approach, 4th Edition
by Stuart Russell
Complete Chapter Solutions Manual
are included (Ch 1 to 28)
** Immediate Download
** Swift Response
** All Chapters included
,Table of Contents are given below
1.Introduction
2.Intelligent Agents
3.Solving Problems by Searching
4.Search in Complex Environments
5.Adversarial Search and Games
6.Constraint Satisfaction Problems
7.Logical Agents
8.First-Order Logic
9.Inference in First-Order Logic
10.Knowledge Representation
11.Automated Planning
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
19.Learning from Examples
20.Learning Probabilistic Models
21.Deep Learning
22.Reinforcement Learning
23.Natural Language Processing
24.Deep Learning for Natural Language Processing
25.Computer Vision
26.Robotics
27.Philosophy and Ethics of AI
28.The Future of AI
,Solutions Manual organized in reverse order, with the last chapter displayed first, to ensure that all
chapters are included in this document. (Complete Chapters included Ch28-1)
EXERCISES 28
THE FUTURE OF AI
[[need exercises]]
28.1 AI Components
Exercise 28.1.#FUTP
Compare the price of various component technologies used in AI, and create charts for
how those prices have changed over the last twenty years.
The answers are changing quickly, and will depend on exactly what month this assignment
is given. Components to compare include CPUs, GPUs, other specialized processors, cloud
computing services, light sensors such as digital cameras, active sensors such as lidar and
radar, and actuators such as robot arms, motors, and chassis.
[[need exercises]]
28.2 AI Architectures
[[need exercises]]
© 2023 Pearson Education, Hoboken, NJ. All rights reserved.
, EXERCISES 27
PHILOSOPHY, ETHICS, AND SAFETY
OF AI
27.1 The Limits of AI
Exercise 27.1.#DISA
Go through Turing’s list of alleged “disabilities” of machines, identifying which have
been achieved, which are achievable in principle by a program, and which are still problem-
atic because they require conscious mental states.
We will take the disabilities (see page 949) one at a time. Note that this exercise might be
better as a class discussion rather than written work.
a. be kind: Certainly there are programs that are polite and helpful, but to be kind requires
an intentional state, so this one is problematic.
b. resourceful: Resourceful means “clever at finding ways of doing things.” Many pro-
grams meet this criteria to some degree: a compiler can be clever making an optimiza-
tion that the programmer might not ever have thought of; a database program might
cleverly create an index to make retrievals faster; a checkers or backgammon program
learns to play as well as any human. One could argue whether the machines are “re-
ally” clever or just seem to be, but most people would agree this requirement has been
achieved.
c. beautiful: Its not clear if Turing meant to be beautiful or to create beauty, nor is it clear
whether he meant physical or inner beauty. Certainly the many industrial artifacts in
the New York Museum of Modern Art, for example, are evidence that a machine can be
beautiful. There are also programs that have created art. One of the best known of these
is chronicled in Aaron’s code: Meta-art, artificial intelligence, and the work of Harold
Cohen (McCorduck, 1991). There have been many instances of computers making art
since, such as with the music of David Cope (Cope, 2008) or the images of Google’s
Deep Dream (Mordvinstev, 2015).
d. friendly This appears to fall under the same category as kind.
e. have initiative Interestingly, there is now a serious debate whether software should take
initiative. The whole field of software agents says that it should; critics such as Ben
Schneiderman say that to achieve predictability, software should only be an assistant,
not an autonomous agent. Notice that the debate over whether software should have
initiative presupposes that it has initiative.
© 2023 Pearson Education, Hoboken, NJ. All rights reserved.
