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 ...
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III Knowledge, reasoning, and planning
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7 Logical Agents ...
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8 First-Order Logic ...
|||| | | | |
9 Inference in First-Order Logic ...
|||| | | | | |
10 Knowledge Representation ...
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11 Automated Planning ...
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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 ...
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21 Deep Learning ...
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22 Reinforcement Learning ...
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VI Communicating, perceiving, and acting
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23 Natural Language Processing ...
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24 Deep Learning for Natural Language Processing ...
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25 Computer Vision ...
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26 Robotics ...
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VII Conclusions
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27 Philosophy, Ethics, and Safety of AI ...
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28 The Future of AI
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, EXERCISES | |
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?
| | |
Exercise |1.1.#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. We |define |logical |reasoning |as |the |a |process |of |deriving |new |sentences |from |old, |such
|that |the |new |sentences |are |necessarily |true |if |the |old |ones |are |true. |(Notice |that |does |not
|refer |to |any |specific |syntax |or |formal |language, |but |it |does |require |a |well-defined |notion |of
|truth.)
Exercise |1.1.#TURI
Read |Turing’s |original |paper |on |AI |(Turing, |1950). | In |the |paper, |he |discusses |several
|objections |to |his |proposed |enterprise |and |his |test |for |intelligence. |Which |objections |still |carry
© |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
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?
| | |
Exercise |1.1.#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. We |define |logical |reasoning |as |the |a |process |of |deriving |new |sentences |from |old, |such
|that |the |new |sentences |are |necessarily |true |if |the |old |ones |are |true. |(Notice |that |does |not
|refer |to |any |specific |syntax |or |formal |language, |but |it |does |require |a |well-defined |notion |of
|truth.)
Exercise |1.1.#TURI
Read |Turing’s |original |paper |on |AI |(Turing, |1950). | In |the |paper, |he |discusses |several
|objections |to |his |proposed |enterprise |and |his |test |for |intelligence. |Which |objections |still |carry
© |2023 |Pearson |Education, |Hoboken, |NJ. |All |rights
|reserved.
