Artificial intelligence: a modern approach
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boston [u.a.]
Pearson
2010
|
| Edition: | 3. ed., internat. ed. |
| Series: | Prentice Hall series in artificial intelligence
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XVIII, 1132 S. Ill., graph. Darst. |
| ISBN: | 9780132071482 0132071487 |
Staff View
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| adam_text |
Contents
I Artificial Intelligence
1
Introduction
1
1.1
What Is
AI?. 1
1.2
The Foundations of Artificial Intelligence
. 5
1.3
The History of Artificial Intelligence
. 16
1.4
The State of the Art
. 28
1.5
Summary, Bibliographical and Historical Notes, Exercises
. 29
2
Intelligent Agents
34
2.1
Agents and Environments
. 34
2.2
Good Behavior: The Concept of Rationality
. 36
2.3
The Nature of Environments
. 40
2.4
The Structure of Agents
. 46
2.5
Summary, Bibliographical and Historical Notes, Exercises
. 59
II Problem-solving
3
Solving Problems by Searching
64
3.1
Problem-Solving Agents
. 64
3.2
Example Problems
. 69
3.3
Searching for Solutions
. 75
3.4
Uninformed Search Strategies
. 81
3.5
Informed (Heuristic) Search Strategies
. 92
3.6
Heuristic Functions
. 102
3.7
Summary, Bibliographical and Historical Notes, Exercises
. 108
4
Beyond Classical Search
120
4.1
Local Search Algorithms and Optimization Problems
. 120
4.2
Local Search in Continuous Spaces
. 129
4.3
Searching with Nondeterministic Actions
. 133
4.4
Searching with Partial Observations
. 138
4.5
Online Search Agents and Unknown Environments
. 147
4.6
Summary, Bibliographical and Historical Notes, Exercises
. 153
5
Adversarial Search
161
5.1
Games
. 161
5.2
Optimal Decisions in Games
. 163
5.3
Alpha-Beta Pruning
. 167
5.4
Imperfect
Real-Time
Decisions
. 171
5.5
Stochastic Games
. 177
xiii
xiv Contents
5.6
Partially
Observable
Games. 180
5.7
State-of-the-Art
Game Programs
. 185
5.8
Alternative Approaches
. 187
5.9
Summary, Bibliographical and Historical Notes, Exercises
. 189
6
Constraint Satisfaction Problems
202
6.1
Defining Constraint Satisfaction Problems
. 202
6.2
Constraint Propagation: Inference in CSPs
. 208
6.3
Backtracking Search for CSPs
. 214
6.4
Local Search for CSPs
. 220
6.5
The Structure of Problems
. 222
6.6
Summary, Bibliographical and Historical Notes, Exercises
. 227
HI Knowledge, reasoning, and planning
7
Logical Agents
234
7.1
Knowledge-Based Agents
. 235
7.2
The Wumpus World
. 236
7.3
Logic
. 240
7.4
Prepositional Logic: A Very Simple Logic
. 243
7.5
Prepositional Theorem Proving
. 249
7.6
Effective Prepositional Model Checking
. 259
7.7
Agents Based on Prepositional Logic
. 265
7.8
Summary, Bibliographical and Historical Notes, Exercises
. 274
8
First-Order Logic
285
8.1
Representation Revisited
. 285
8.2
Syntax and Semantics of First-Order Logic
. 290
8.3
Using First-Order Logic
. 300
8.4
Knowledge Engineering in First-Order Logic
. 307
8.5
Summary, Bibliographical and Historical Notes, Exercises
. 313
9
Inference in First-Order Logic
322
9.1
Prepositional vs. First-Order Inference
. 322
9.2
Unification and Lifting
. 325
9.3
Forward Chaining
. 330
9.4
Backward Chaining
. 337
9.5
Resolution
. 345
9.6
Summary, Bibliographical and Historical Notes, Exercises
. 357
10
Classical Planning
366
10.1
Definition of Classical Planning
. 366
10.2
Algorithms for Planning as State-Space Search
. 373
10.3
Planning Graphs
. 379
Contents xv
10.4
Other Classical Planning Approaches
. 387
10.5
Analysis of Planning Approaches
. 392
10.6
Summary, Bibliographical and Historical Notes, Exercises
. 393
11
Planning and Acting in the Real World
401
11.1
Time, Schedules, and Resources
. 401
11.2
Hierarchical Planning
. 406
11.3
Planning and Acting in Nondeterministic Domains
. 415
11.4
Multiagent Planning
. 425
11.5
Summary, Bibliographical and Historical Notes, Exercises
. 430
12
Knowledge Representation
437
12.1
Ontological Engineering
. 437
12.2
Categories and Objects
. 440
12.3
Events
. 446
12.4
Mental Events and Mental Objects
. 450
12.5
Reasoning Systems for Categories
. 453
12.6
Reasoning with Default Information
. 458
12.7
The Internet Shopping World
. 462
12.8
Summary, Bibliographical and Historical Notes, Exercises
. 467
IV Uncertain knowledge and reasoning
13
Quantifying Uncertainty
480
13.1
Acting under Uncertainty
. 480
13.2
Basic Probability Notation
. 483
13.3
Inference Using Full Joint Distributions
. 490
13.4
Independence
. 494
13.5
Bayes'
Rule and Its Use
. 495
13.6
The Wumpus World Revisited
. 499
13.7
Summary, Bibliographical and Historical Notes, Exercises
. 503
14
Probabilistic Reasoning
510
14.1
Representing Knowledge in an Uncertain Domain
. 510
14.2
The Semantics of Bayesian Networks
. 513
14.3
Efficient Representation of Conditional Distributions
. 518
14.4
Exact Inference in Bayesian Networks
. 522
14.5
Approximate Inference in Bayesian Networks
. 530
14.6
Relational and First-Order Probability Models
. 539
14.7
Other Approaches to Uncertain Reasoning
. 546
14.8
Summary, Bibliographical and Historical Notes, Exercises
. 551
15
Probabilistic Reasoning over Time
566
15.1
Time and Uncertainty
. 566
xvi
Contents
15.2
Inference in Temporal Models
. 570
15.3
Hidden Markov Models
. 578
15.4
Kalman
Filters
. 584
15.5
Dynamic Bayesian Networks
. 590
15.6
Keeping Track of Many Objects
. 599
15.7
Summary, Bibliographical and Historical Notes, Exercises
. 603
16
Making Simple Decisions
610
16.1
Combining Beliefs and Desires under Uncertainty
. 610
16.2
The Basis of Utility Theory
. 611
16.3
Utility Functions
. 615
16.4
Multiattribute Utility Functions
. 622
16.5
Decision Networks
. 626
16.6
The Value of Information
. 628
16.7
Decision-Theoretic Expert Systems
. 633
16.8
Summary, Bibliographical and Historical Notes, Exercises
. 636
17
Making Complex Decisions
645
17.1
Sequential Decision Problems
. 645
17.2
Value Iteration
. 652
17.3
Policy Iteration
. 656
17.4
Partially Observable MDPs
. 658
17.5
Decisions with Multiple Agents: Game Theory
. 666
17.6
Mechanism Design
. 679
17.7
Summary, Bibliographical and Historical Notes, Exercises
. 684
V Learning
18
Learning from Examples
693
18.1
Forms of Learning
. 693
18.2
Supervised Learning
. 695
18.3
Learning Decision Trees
. 697
18.4
Evaluating and Choosing the Best Hypothesis
. 708
18.5
The Theory of Learning
. 713
18.6
Regression and Classification with Linear Models
. 717
18.7
Artificial Neural Networks
. 727
18.8
Nonparametric Models
. 737
18.9
Support Vector Machines
. 744
18.10
Ensemble Learning
.
74g
18.11
Practical Machine Learning
. 753
18.12
Summary, Bibliographical and Historical Notes, Exercises
. 757
19
Knowledge in Learning
75g
19.1
A Logical Formulation of Learning
.
76g
Contents xvii
19.2 Knowledge in
Learning
. 777
19.3
Explanation-Based Learning
. 780
19.4
Learning Using Relevance Information
. 784
19.5
Inductive Logic Programming
. 788
19.6
Summary, Bibliographical and Historical Notes, Exercises
. 797
20
Learning Probabilistic Models
802
20.1
Statistical Learning
. 802
20.2
Learning with Complete Data
. 806
20.3
Learning with Hidden Variables: The EM Algorithm
. 816
20.4
Summary, Bibliographical and Historical Notes, Exercises
. 825
21
Reinforcement Learning
830
21.1
Introduction
. 830
21.2
Passive Reinforcement Learning
. 832
21.3
Active Reinforcement Learning
. 839
21.4
Generalization in Reinforcement Learning
. 845
21.5
Policy Search
. 848
21.6
Applications of Reinforcement Learning
. 850
21.7
Summary, Bibliographical and Historical Notes, Exercises
. 853
VI Communicating, perceiving, and acting
22
Natural Language Processing
860
22.1
Language Models
. 860
22.2
Text Classification
. 865
22.3
Information Retrieval
. 867
22.4
Information Extraction
. 873
22.5
Summary, Bibliographical and Historical Notes, Exercises
. 882
23
Natural Language for Communication
888
23.1
Phrase Structure Grammars
. 888
23.2
Syntactic Analysis (Parsing)
. 892
23.3
Augmented Grammars and Semantic Interpretation
. 897
23.4
Machine Translation
. 907
23.5
Speech Recognition
. 912
23.6
Summary, Bibliographical and Historical Notes, Exercises
. 918
24
Perception
928
24.1
Image Formation
. 929
24.2
Early Image-Processing Operations
. 935
24.3
Object Recognition by Appearance
. 942
24.4
Reconstructing the
3D
World
. 947
24.5
Object Recognition from Structural Information
. 957
xviii
Contents
24.6
Using Vision
. 961
24.7
Summary, Bibliographical and Historical Notes, Exercises
. 965
25
Robotics
971
25.1
Introduction
. 971
25.2
Robot Hardware
. 973
25.3
Robotic Perception
. 978
25.4
Planning to Move
. 986
25.5
Planning Uncertain Movements
. 993
25.6
Moving
. 997
25.7
Robotic Software Architectures
. 1003
25.8
Application Domains
. 1006
25.9
Summary, Bibliographical and Historical Notes, Exercises
. 1010
VII
Conclusions
26
Philosophical Foundations
1020
26.1
Weak
AI:
Can Machines Act Intelligently?
. 1020
26.2
Strong
AI:
Can Machines Really Think?
. 1026
26.3
The Ethics and Risks of Developing Artificial Intelligence
. 1034
26.4
Summary, Bibliographical and Historical Notes, Exercises
. 1040
27
AI:
The Present and Future
1044
27.1
Agent Components
. 1044
27.2
Agent Architectures
. 1047
27.3
Are We Going in the Right Direction?
. 1049
27.4
What If
AI
Does Succeed?
. 1051
A Mathematical background
1053
A.I Complexity Analysis and O() Notation
. 1053
A.2 Vectors, Matrices, and Linear Algebra
. 1055
A.3 Probability Distributions
. 1057
В
Notes on Languages and Algorithms
1060
B.I Defining Languages with Backus-Naur Form (BNF)
. 1060
B.2 Describing Algorithms with Pseudocode
. 1061
B.3 Online Help
. 1062
Bibliography
1063
Index
1095 |
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| spellingShingle | Russell, Stuart J. 1962- Norvig, Peter 1956- Artificial intelligence a modern approach Künstliche Intelligenz (DE-588)4033447-8 gnd |
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| title | Artificial intelligence a modern approach |
| title_auth | Artificial intelligence a modern approach |
| title_exact_search | Artificial intelligence a modern approach |
| title_full | Artificial intelligence a modern approach Stuart J. Russell and Peter Norvig |
| title_fullStr | Artificial intelligence a modern approach Stuart J. Russell and Peter Norvig |
| title_full_unstemmed | Artificial intelligence a modern approach Stuart J. Russell and Peter Norvig |
| title_short | Artificial intelligence |
| title_sort | artificial intelligence a modern approach |
| title_sub | a modern approach |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Künstliche Intelligenz Aufgabensammlung Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020426659&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT russellstuartj artificialintelligenceamodernapproach AT norvigpeter artificialintelligenceamodernapproach |
Indexes
THWS Würzburg Zentralbibliothek Lesesaal
| Call Number: |
1000 ST 300 R967 A7(3)st |
|---|---|
| Copy 1 | ausleihbar Available Place a Hold |
THWS Würzburg Teilbibliothek SHL, Raum I.2.11
| Call Number: |
1340 ST 300 R961(3)st |
|---|---|
| Copy 1 | ausleihbar Available Place a Hold |