Artificial Intelligence: A Modern Approach, Global Edition
3rd Edition
Published on 18. May 2016
Book
Paperback/Softback
1152 pages
978-1-292-15396-4 (ISBN)
Description
For one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.
The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.
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Other editions
New editions
Stuart Russell | Peter Norvig
Artificial Intelligence: A Modern Approach, Global Edition
A Modern Approach, Global Edition
Book
05/2021
4th Edition
Pearson Education Limited
€90.49
Available immediately
Previous edition
Stuart Russell | Peter Norvig
Artificial Intelligence: Pearson New International Edition
A Modern Approach
Book
08/2013
3rd Edition
Pearson Education Limited
€75.51
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Content
I. Artificial Intelligence
1. Introduction2. Intelligent Agents
II. Problem-solving
3. Solving Problems by Searching
4. Beyond Classical Search
5. Adversarial Search
6. Constraint Satisfaction Problems
III. Knowledge, Reasoning, and Planning
7. Logical Agents
8. First-Order Logic
9. Inference in First-Order Logic
10. Classical Planning
11. Planning and Acting in the Real World
12 Knowledge Representation
IV. Uncertain Knowledge and Reasoning13. Quantifying Uncertainty
14. Probabilistic Reasoning
15. Probabilistic Reasoning over Time
16. Making Simple Decisions
17. Making Complex Decisions
V. Learning18. Learning from Examples
19. Knowledge in Learning
20. Learning Probabilistic Models
21. Reinforcement Learning
VI. Communicating, Perceiving, and Acting22. Natural Language Processing
23. Natural Language for Communication
24. Perception
25. Robotics
VII. Conclusions
26 Philosophical Foundations
27. AI: The Present and Future
A. Mathematical Background
B. Notes on Languages and Algorithms
Bibliography
Index
1. Introduction2. Intelligent Agents
II. Problem-solving
3. Solving Problems by Searching
4. Beyond Classical Search
5. Adversarial Search
6. Constraint Satisfaction Problems
III. Knowledge, Reasoning, and Planning
7. Logical Agents
8. First-Order Logic
9. Inference in First-Order Logic
10. Classical Planning
11. Planning and Acting in the Real World
12 Knowledge Representation
IV. Uncertain Knowledge and Reasoning13. Quantifying Uncertainty
14. Probabilistic Reasoning
15. Probabilistic Reasoning over Time
16. Making Simple Decisions
17. Making Complex Decisions
V. Learning18. Learning from Examples
19. Knowledge in Learning
20. Learning Probabilistic Models
21. Reinforcement Learning
VI. Communicating, Perceiving, and Acting22. Natural Language Processing
23. Natural Language for Communication
24. Perception
25. Robotics
VII. Conclusions
26 Philosophical Foundations
27. AI: The Present and Future
A. Mathematical Background
B. Notes on Languages and Algorithms
Bibliography
Index