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SOLUTIONS & INSTRUCTOR MANUAL Artificial Intelligence: A Modern Approach, 4th Edition by Peter Norvig and Stuart Russell, Chapters 1 – 28

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SOLUTIONS & INSTRUCTOR MANUAL Artificial Intelligence: A Modern Approach, 4th Edition by Peter Norvig and Stuart Russell, Chapters 1 – 28 SOLUTIONS & INSTRUCTOR MANUAL Artificial Intelligence: A Modern Approach, 4th Edition by Peter Norvig and Stuart Russell, Chapters 1 – 28

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Artificial Intelligence: A Modern Approach, 4th Edition




Instructor’s Manual:
Exercise Solutions
for

Artificial Intelligence
A Modern Approach
Fourth Edition



Stuart J. Russell and Peter Norvig




Solution Manual Page

, Artificial Intelligence: A Modern Approach, 4th Edition




Solutions for Chapter 1
Introduction



1.1

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 apply knowledge in order to
perform better in an environment.‖
b. We define artificial intelligence as the study and construction of agent programs that
perform well in a given environment, for a given agent architecture.
c. We define an agent as an entity that takes action in response to percepts from an envi-
ronment.

1.2 See the solution for exercise 26.1 for some discussion of potential objections.
The probability of fooling an interrogator depends on just how unskilled the interroga-
tor is. One entrant in the 2002 Loebner prize competition (which is not quite a real Turing
Test) did fool one judge, although if you look at the transcript, it is hard to imagine what
that judge was thinking. There certainly have been examples of a chatbot or other online
agent fooling humans. For example, see See Lenny Foner’s account of the Julia chatbot
at foner.www.media.mit.edu/people/foner/Julia/. We’d say the chance today is something
like 10%, with the variation depending more on the skill of the interrogator rather than the
program. In 50 years, we expect that the entertainment industry (movies, video games, com-
mercials) will have made sufficient investments in artificial actors to create very credible
impersonators.

1.3 The 2002 Loebner prize (www.loebner.net) went to Kevin Copple’s program ELLA. It
consists of a prioritized set of pattern/action rules: if it sees a text string matching a certain
pattern, it outputs the corresponding response, which may include pieces of the current or
past input. It also has a large database of text and has the Wordnet online dictionary. It is
therefore using rather rudimentary tools, and is not advancing the theory of AI. It is provid-

Solution Manual Page

, Artificials2Intelligence:s2As2Moderns2Approach,s24ths2E
dition




ings2evidences2ons2thes2numbers2ands2types2ofs2ruless2thats2ares2sufficients2fors2producings
2ones2types2ofs2conversation.

1.4 No.s2Its2meanss2thats2AIs2systemss2shoulds2avoids2tryings2tos2solves2intractables2proble
ms.s2Usually,s2thiss2meanss2theys2cans2onlys2approximates2optimals2behavior.s2Notices2thats2
humanss2don’ts2solves2NP-
s2completes2problemss2either.s2 Sometimess2theys2ares2goods2ats2solvings2specifics2instancess2

withs2as2lots2of
structure,s2perhapss2withs2thes2aids2ofs2backgrounds2knowledge.s2AIs2systemss2shoulds2atte
mpts2tos2dos2thes2same.

1.5 No.s2IQs2tests2scoress2correlates2wells2withs2certains2others2measures,s2suchs2ass2successs
2ins2college,s2buts2onlys2ifs2they’res2measurings2fairlys2normals2humans.s2Thes2IQs2tests2does

n’ts2measures2everything.s2As2programs2thats2iss2specializeds2onlys2fors2IQs2testss2(ands2sp
ecializeds2furthers2onlys2fors2thes2analogys2part)s2woulds2verys2likelys2performs2poorlys2on
s2others2measuress2ofs2intelligence.s2Sees2Thes2Mismea-

s2sures2ofs2Mans2bys2Stephens2Jays2Gould,s2Norton,s21981s2ors2Multiples2intelligences:s2th

es2theorys2ins2practices2bys2Howards2Gardner,s2Basics2Books,s21993s2fors2mores2ons2IQs2t
ests,s2whats2theys2measure,s2ands2whats2others2aspectss2theres2ares2tos2―intelligence.‖

1.6 Justs2ass2yous2ares2unawares2ofs2alls2thes2stepss2thats2gos2intos2makings2yours2hearts2
beat,s2yous2ares2alsos2unawares2ofs2mosts2ofs2whats2happenss2ins2yours2thoughts.s2Yous2dos2
haves2as2consciouss2awarenesss2ofs2somes2ofs2yours2thoughts2processes,s2buts2thes2majority
s2remainss2opaques2tos2yours2consciousness.s2Thes2fields2ofs2psychoanalysiss2iss2baseds2on

s2thes2ideas2thats2ones2needss2traineds2professionals2helps2tos2analyzes2one’ss2owns2thought

s.

1.7

a. (ping-
pong)s2As2reasonables2levels2ofs2proficiencys2wass2achieveds2bys2Andersson’ss2robots2(
An-s2dersson,s21988).
b. (drivings2ins2Cairo)s2No.s2Althoughs2theres2hass2beens2as2lots2ofs2progresss2ins2autom
ateds2driving,s2alls2suchs2systemss2currentlys2relys2ons2certains2relativelys2constants2c

Solutions2Manu Page
al

, Artificials2Intelligence:s2As2Moderns2Approach,s24ths2E
lues:s2thats2thedition
s2roads2hass2shoulderss2ands2as2centers2line,s2thats2thes2cars2aheads2wills

2travels2as2predictables2course,s2thats2carss2wills2keeps2tos2theirs2sides2ofs2thes2road,s2a

nds2sos2on.s2Tos2ours2knowledge,s2nones2ares2ables2tos2avoids2obstacless2ors2others2cars
s2ors2tos2changes2laness2ass2appropriate;s2theirs2skillss2ares2mostlys2confineds2tos2stayings

2ins2ones2lanes2ats2constants2speed.s2Drivings2ins2downtowns2Cairos2iss2toos2unpredictab

les2fors2anys2ofs2theses2tos2work.
c. (shoppings2ats2thes2market)s2No.s2Nos2robots2cans2currentlys2puts2togethers2thes2taskss2o
fs2movings2ins2as2crowdeds2environment,s2usings2visions2tos2identifys2as2wides2varietys2
ofs2objects,s2ands2graspings2thes2objectss2(includings2squishables2vegetables)s2withouts
2damagings2them.s2Thes2components2piecess2ares2nearlys2ables2tos2handles2thes2indivi

duals2tasks,s2buts2its2woulds2takes2as2majors2integra-
s2tions2efforts2tos2puts2its2alls2together.




Solutions2Manu Page
al

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Editorial: 2015 ISBN: 9781632403582 Edición: Desconocido

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