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SOLUTIONS & INSTRUCTOR MANUAL for Artificial Intelligence A Modern Approach, 4th Edition By Stuart Russell ISBN 978-0134610993 COMPLETE GUIDE | A+ GRADE ASSURED!!! LATEST UPDATE!!!! GUARANTEED SUCCESS!!!!

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SOLUTIONS & INSTRUCTOR MANUAL for Artificial Intelligence A Modern Approach, 4th Edition By Stuart Russell ISBN 978-0134610993 COMPLETE GUIDE | A+ GRADE ASSURED!!! LATEST UPDATE!!!! GUARANTEED SUCCESS!!!!

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Instructor’s Solution Manual G G




ArtificialIntelligence G G




A Modern Approach G G




FourthEdition G




Stuart J. Russell and Peter Norvig
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with contributions from
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Nalin Chhibber, Ernest Davis, Nicholas J. Hay, Jared Moore, Alex Rudnick, Mehran S
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ahami, Xiaocheng Mesut Yang, and Albert Yu G G G G G G




This solution manual is intended for the instructor of a class. Students should use the online site f
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or exercises at aimacode.github.io/aima-
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exercises. That site is open for anyone to use. It offers solutions for some but not all of the exe
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rcises; an instructor can check there to see which ones have solutions. The exercises are online r
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ather than in the textbook itself because (a) the textbook is long enough as is, and (b) we wanted
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to be able to update the exercises frequently.
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Copyright © 2022 G G




© 2023 Pearson Education, Hoboken, NJ. All rights reserved.
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,EXERCISES G G




1
INTRODUCTION
Note that for many of the questions in this chapter, we give references where answers can be foun
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d rather than writing them out—the full answers would be far too long.
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1.1 What Is AI? G G G




Exercise 1.1.#DEFA G




Define in your own words: (a) intelligence, (b) artificial intelligence, (c) agent, (d) ra-
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tionality, (e) logical reasoning.
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a. Dictionary definitions of intelligence talk about “the capacity to acquire and apply knowl
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edge” or “the faculty of thought and reason” or “the ability to comprehend and profit from
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experience.” These are all reasonable answers, but if we want something quantifiable we
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would use something like “the ability to act successfully across a wide range of objectives
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in complex environments.”
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b. We define artificial intelligence as the study and construction of agent programs that per
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form well in a given class of environments, for a given agent architecture; they do the righ
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t thing. An important part of that is dealing with the uncertainty of what the current state is
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, what the outcome of possible actions might be, and what is it that we really desire.
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c. We define an agent as an entity that takes action in response to percepts from an envi-
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ronment.G




d. We define rationality as the property of a system which does the “right thing” given what
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it knows. See Section 2.2 for a more complete discussion. The basic concept is perfect rat
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ionality; Section ?? describes the impossibility of achieving perfect rational-
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ity and proposes an alternative definition.
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e. We define logical reasoning as the a process of deriving new sentences from old, such that
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the new sentences are necessarily true if the old ones are true. (Notice that does not refer to a
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ny specific syntax or formal language, but it does require a well-defined notion of truth.)
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Exercise 1.1.#TURI G




Read Turing’s original paper on AI (Turing, 1950). In the paper, he discusses several objectio
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ns to his proposed enterprise and his test for intelligence. Which objections still carry
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© 2023 Pearson Education, Hoboken, NJ. All rights reserved.
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, Section 1.1 What Is AI? G G G G G 3



weight? Are his refutations valid? Can you think of new objections arising from develop-
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ments since he wrote the paper? In the paper, he predicts that, by the year 2000, a computer will
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have a 30% chance of passing a five-
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minute Turing Test with an unskilled interrogator. What chance do you think a computer would
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have today? In another 25 years?
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See the solution for exercise 26.1 for some discussion of potential objections.
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The probability of fooling an interrogator depends on just how unskilled the interrogator is.
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A few entrants in the Loebner prize competitions have fooled judges, although if you look at the
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transcripts, it looks like the judges were having fun rather than taking their job seriously. There
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certainly have been examples of a chatbot or other online agent fooling humans. For example, s
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ee the description of the Julia chatbot at www.lazytd.com/lti/ julia/. We’d say the ch
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ance today is something like 10%, with the variation depending more on the skill of the interrog
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ator rather than the program. In 25 years, we expect that the entertainment industry (movies, vi
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deo games, commercials) will have made sufficient investments in artificial actors to create ver
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y credible impersonators.
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Note that governments and international organizations are seriously considering rules that req
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uire AI systems to be identified as such. In California, it is already illegal for machines to imperso
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nate humans in certain circumstances.
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Exercise 1.1.#REFL G




Are reflex actions (such as flinching from a hot stove) rational? Are they intelligent?
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Yes, they are rational, because slower, deliberative actions would tend to result in more da
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mage to the hand. If “intelligent” means “applying knowledge” or “using thought and reasonin
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g” then it does not require intelligence to make a reflex action.
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Exercise 1.1.#SYAI G




To what extent are the following computer systems instances of artificial intelligence:
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• Supermarket bar code scanners. G G G




• Web search engines. G G




• Voice-activated telephone menus. G G




• Spelling and grammar correction features in word processing programs.
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• Internet routing algorithms that respond dynamically to the state of the network.
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• Although bar code scanning is in a sense computer vision, these are not AI systems. The p
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roblem of reading a bar code is an extremely limited and artificial form of visual interpreta
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tion, and it has been carefully designed to be as simple as possible, given the hardware.
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• In many respects. The problem of determining the relevance of a web page to a query is a p
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roblem in natural language understanding, and the techniques are related to those
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© 2023 Pearson Education, Hoboken, NJ. All rights reserved.
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, 4 Exercises 1 Introduction G G G




we will discuss in Chapters 23 and 24. Search engines also use clustering techniques anal
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ogous to those we discuss in Chapter 20. Likewise, other functionalities provided by a sea
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rch engines use intelligent techniques; for instance, the spelling corrector uses a form of dat
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a mining based on observing users’ corrections of their own spelling errors. On the other ha
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nd, the problem of indexing billions of web pages in a way that allows retrieval in seconds
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is a problem in database design, not in artificial intelligence.
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• To a limited extent. Such menus tends to use vocabularies which are very limited –
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e.g. the digits, “Yes”, and “No” — G G G G G G




and within the designers’ control, which greatly simplifies the problem. On the other hand
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, the programs must deal with an uncontrolled space of all kinds of voices and accents. Mo
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dern digital assistants like Siri and the Google Assistant make more use of artificial intelli
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gence techniques, but still have a limited repetoire.
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• Slightly at most. The spelling correction feature here is done by string comparison to a fixe
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d dictionary. The grammar correction is more sophisticated as it need to use a set of rather co
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mplex rules reflecting the structure of natural language, but still this is a very limited and f
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ixed task. G




The spelling correctors in search engines would be considered much more nearly inst
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ances of AI than the Word spelling corrector are, first, because the task is much more dyna
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mic – G




search engine spelling correctors deal very effectively with proper names, which are dete
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cted dynamically from user queries – and, second, because of the technique used –
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data mining from user queries vs. string matching.
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• This is borderline. There is something to be said for viewing these as intelligent agents wor
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king in cyberspace. The task is sophisticated, the information available is partial, the techniq
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ues are heuristic (not guaranteed optimal), and the state of the world is dynamic. All of these
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are characteristic of intelligent activities. On the other hand, the task is very far from those n
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ormally carried out in human cognition. In recent years there have been suggestions to base
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more core algorithmic work on machine learning.
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Exercise 1.1.#COGN G




Many of the computational models of cognitive activities that have been proposed involve qui
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te complex mathematical operations, such as convolving an image with a Gaussian or finding a
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minimum of the entropy function. Most humans (and certainly all animals) never learn this kin
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d of mathematics at all, almost no one learns it before college, and almost no one can compute th
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e convolution of a function with a Gaussian in their head. What sense does it make to say that th
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e “vision system” is doing this kind of mathematics, whereas the actual person has no idea how t
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o do it?
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Presumably the brain has evolved so as to carry out this operations on visual images, but the
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mechanism is only accessible for one particular purpose in this particular cognitive task of imag
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e processing. Until about two centuries ago there was no advantage in people (or animals) bein
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g able to compute the convolution of a Gaussian for any other purpose.
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The really interesting question here is what we mean by saying that the “actual person” can
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© 2023 Pearson Education, Hoboken, NJ. All rights reserved.
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