Pearson eText Access Card for Artificial Intelligence: A Modern Approach, Global Edition
4th Edition
Published on 30. March 2022
Software
Product license key
978-1-292-40940-5 (ISBN)
Description
The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, present concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multi agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, present concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multi agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, present concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multi agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
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Persons
Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor and former chair of computer science, director of the Center for Human-Compatible AI, and holder of the Smith-Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was co-winner of the Computers and Thought Award. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science, and Honorary Fellow of Wadham College, Oxford, and an Andrew Carnegie Fellow. He held the Chaire Blaise Pascal in Paris from 2012 to 2014. He has published over 300 papers on a wide range of topics in artificial intelligence. His other books include: The Use of Knowledge in Analogy and Induction, Do the Right Thing: Studies in Limited Rationality (with Eric Wefald), and Human Compatible: Artificial Intelligence and the Problem of Control.
Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASA's research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley. He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and are research faculty member at Berkeley. His other books are: Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX.
The two authors shared the inaugural AAAI/EAAI Outstanding Educator award in 2016.
Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor and former chair of computer science, director of the Center for Human-Compatible AI, and holder of the Smith-Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was co-winner of the Computers and Thought Award. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science, and Honorary Fellow of Wadham College, Oxford, and an Andrew Carnegie Fellow. He held the Chaire Blaise Pascal in Paris from 2012 to 2014. He has published over 300 papers on a wide range of topics in artificial intelligence. His other books include: The Use of Knowledge in Analogy and Induction, Do the Right Thing: Studies in Limited Rationality (with Eric Wefald), and Human Compatible: Artificial Intelligence and the Problem of Control.
Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASA's research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley. He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and are research faculty member at Berkeley. His other books are: Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX.
The two authors shared the inaugural AAAI/EAAI Outstanding Educator award in 2016.
Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASA's research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley. He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and are research faculty member at Berkeley. His other books are: Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX.
The two authors shared the inaugural AAAI/EAAI Outstanding Educator award in 2016.
Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor and former chair of computer science, director of the Center for Human-Compatible AI, and holder of the Smith-Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was co-winner of the Computers and Thought Award. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science, and Honorary Fellow of Wadham College, Oxford, and an Andrew Carnegie Fellow. He held the Chaire Blaise Pascal in Paris from 2012 to 2014. He has published over 300 papers on a wide range of topics in artificial intelligence. His other books include: The Use of Knowledge in Analogy and Induction, Do the Right Thing: Studies in Limited Rationality (with Eric Wefald), and Human Compatible: Artificial Intelligence and the Problem of Control.
Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASA's research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley. He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and are research faculty member at Berkeley. His other books are: Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX.
The two authors shared the inaugural AAAI/EAAI Outstanding Educator award in 2016.
Content
Part I: ArtificialIntelligence
1. Introduction
1.1 What Is AI?
1.2 The Foundations of Artificial Intelligence
1.3 The History of Artificial Intelligence
1.4 The State of the Art
1.5 Risks and Benefits of AI
2. Intelligent Agents
2.1 Agents and Environments
2.2 Good Behavior: The Concept of Rationality
2.3 The Nature of Environments
2.4 The Structure of Agents
Part II: Problem Solving
3. Solving Problems by Searching
3.1 Problem-Solving Agents
3.2 Example Problems
3.3 Search Algorithms
3.4 Uninformed Search Strategies
3.5 Informed (Heuristic) Search Strategies
3.6 Heuristic Functions
4. Search in Complex Environments
4.1 Local Search and Optimization Problems
4.2 Local Search in Continuous Spaces
4.3 Search with Nondeterministic Actions
4.4 Search in Partially Observable Environments
4.5 Online Search Agents and Unknown Environments
5. Constraint Satisfaction Problems
5.1 Defining Constraint Satisfaction Problems
5.2 Constraint Propagation: Inference in CSPs
5.3 Backtracking Search for CSPs
5.4 Local Search for CSPs
5.5 The Structure of Problems
6. Adversarial Search and Games
6.1 Game Theory
6.2 Optimal Decisions in Games
6.3 Heuristic Alpha--Beta Tree Search
6.4 Monte Carlo Tree Search
6.5 Stochastic Games
6.6 Partially Observable Games
6.7 Limitations of Game Search Algorithms
Part III: Knowledge and Reasoning
7. Logical Agents
7.1 Knowledge-Based Agents
7.2 The Wumpus World
7.3 Logic
7.4 Propositional Logic: A Very Simple Logic
7.5 Propositional Theorem Proving
7.6 Effective Propositional Model Checking
7.7 Agents Based on Propositional Logic
8. First-Order Logic
8.1 Representation Revisited
8.2 Syntax and Semantics of First-Order Logic
8.3 Using First-Order Logic
8.4 Knowledge Engineering in First-Order Logic
9. Inference in First-Order Logic
9.1 Propositional vs.~First-Order Inference
9.2 Unification and First-Order Inference
9.3 Forward Chaining
9.4 Backward Chaining
9.5 Resolution
10. Knowledge Representation
10.1 Ontological Engineering
10.2 Categories and Objects
10.3 Events
10.4 Mental Objects and Modal Logic
10.5 Reasoning Systems for Categories
10.6 Reasoning with Default Information
11. Automated Planning
11.1 Definition of Classical Planning
11.2 Algorithms for Classical Planning
11.3 Heuristics for Planning
11.4 Hierarchical Planning
11.5 Planning and Acting in Nondeterministic Domains
11.6 Time, Schedules, and Resources
11.7 Analysis of Planning Approaches
12. Quantifying Uncertainty
12.1 Acting under Uncertainty
12.2 Basic Probability Notation
12.3 Inference Using Full Joint Distributions
12.4 Independence
12.5 Bayes' Rule and Its Use
12.6 Naive Bayes Models
12.7 The Wumpus World Revisited
Part IV: Uncertain Knowledge and Reasoning
13. Probabilistic Reasoning
13.1 Representing Knowledge in an Uncertain Domain
13.2 The Semantics of Bayesian Networks
13.3 Exact Inference in Bayesian Networks
13.4 Approximate Inference for Bayesian Networks
13.5 Causal Networks
14. Probabilistic Reasoning over Time
14.1 Time and Uncertainty
14.2 Inference in Temporal Models
14.3 Hidden Markov Models
14.4 Kalman Filters
14.5 Dynamic Bayesian Networks
15. Making Simple Decisions
16.1 Combining Beliefs and Desires under Uncertainty
16.2 The Basis of Utility Theory
16.3 Utility Functions
16.4 Multiattribute Utility Functions
16.5 Decision Networks
16.6 The Value of Information
16.7 Unknown Preferences
16. Making Complex Decisions
17.1 Sequential Decision Problems
17.2 Algorithms for MDPs
17.3 Bandit Problems
17.4 Partially Observable MDPs
17.5 Algorithms for solving POMDPs
Part V: Learning
17. Multiagent Decision Making
17.1 Properties of Multiagent Environments
17.2 Non-Cooperative Game Theory
17.3 Cooperative Game Theory
17.4 Making Collective Decisions
18. ProbabilisticProgramming
18.1 Relational Probability Models
18.2 Open-Universe Probability Models
18.3 Keeping Track of a Complex World
18.4 Programs as Probability Models
19. Learning fromExamples
19.1 Forms of Learning
19.2 Supervised Learning
19.3 Learning Decision Trees
19.4 Model Selection and Optimization
19.5 The Theory of Learning
19.6 Linear Regression and Classification
19.7 Nonparametric Models
19.8 Ensemble Learning
19.9 Developing Machine Learning Systems
20. Knowledge inLearning
20.1 A Logical Formulation of Learning
20.2 Knowledge in Learning
20.3 Explanation-Based Learning
20.4 Learning Using Relevance Information
20.5 Inductive Logic Programming
21. LearningProbabilistic Models
21.1 Statistical Learning
21.2 Learning with Complete Data
21.3 Learning with Hidden Variables: The EM Algorithm
22. Deep Learning
22.1 Simple Feedforward Networks
22.2 Mixing and matching models, loss functions andoptimizers
22.3 Loss functions
22.4 Models
22.5 Optimization Algorithms
22.6 Generalization
22.7 Recurrent neural networks
22.8 Unsupervised, semi-supervised and transferlearning
22.9 Applications
Part VI: Communicating, Perceiving, and Acting
23. Reinforcement Learning
23.1 Learning from Rewards
23.2 Passive Reinforcement Learning
23.3 Active Reinforcement Learning
23.4 Safe Exploration
23.5 Generalization in Reinforcement Learning
23.6 Policy Search
23.7 Applications of Reinforcement Learning
24. Natural Language Processing
24.1 Language Models
24.2 Grammar
24.3 Parsing
24.4 Augmented Grammars
24.5 Complications of Real Natural Language
24.6 Natural Language Tasks
25. Deep Learning for Natural Language Processing
25.1 Limitations of Feature-Based NLP Models
25.2 Word Embeddings
25.3 Recurrent Neural Networks
25.4 Sequence-to-sequence Models
25.5 The Transformer Architecture
25.6 Pretraining and Transfer Learning
26. Robotics
26.1 Robots
26.2 Robot Hardware
26.3 What kind of problem is robotics solving?
26.4 Robotic Perception
26.5 Planning and Control
26.6 Planning Uncertain Movements
26.7 Reinforcement Learning in Robotics
26.8 Humans and Robots
26.9 Alternative Robotic Frameworks
26.10 Application Domains
27. Computer Vision
27.1 Introduction
27.2 Image Formation
27.3 Simple Image Features
27.4 Classifying Images
27.5 Detecting Objects
27.6 The 3D World
27.7 Using Computer Vision
Part VII: Conclusions
28. Philosophy and Ethics of AI
28.1 Weak AI: What are the Limits of AI?
28.2 Strong AI: Can Machines Really Think?
28.3 The Ethics of AI
29. The Future of AI
29.1 AI Components
29.2 AI Architectures
Appendix A:Mathematical Background
A.1 Complexity Analysis and O() Notation
A.2 Vectors, Matrices, and Linear Algebra
A.3 Probability Distributions
Appendix B: Notes on Languages and Algorithms
B.1 Defining Languages with Backus--Naur Form (BNF)
B.2 Describing Algorithms with Pseudocode
B.3 Online Supplemental Material
1. Introduction
1.1 What Is AI?
1.2 The Foundations of Artificial Intelligence
1.3 The History of Artificial Intelligence
1.4 The State of the Art
1.5 Risks and Benefits of AI
2. Intelligent Agents
2.1 Agents and Environments
2.2 Good Behavior: The Concept of Rationality
2.3 The Nature of Environments
2.4 The Structure of Agents
Part II: Problem Solving
3. Solving Problems by Searching
3.1 Problem-Solving Agents
3.2 Example Problems
3.3 Search Algorithms
3.4 Uninformed Search Strategies
3.5 Informed (Heuristic) Search Strategies
3.6 Heuristic Functions
4. Search in Complex Environments
4.1 Local Search and Optimization Problems
4.2 Local Search in Continuous Spaces
4.3 Search with Nondeterministic Actions
4.4 Search in Partially Observable Environments
4.5 Online Search Agents and Unknown Environments
5. Constraint Satisfaction Problems
5.1 Defining Constraint Satisfaction Problems
5.2 Constraint Propagation: Inference in CSPs
5.3 Backtracking Search for CSPs
5.4 Local Search for CSPs
5.5 The Structure of Problems
6. Adversarial Search and Games
6.1 Game Theory
6.2 Optimal Decisions in Games
6.3 Heuristic Alpha--Beta Tree Search
6.4 Monte Carlo Tree Search
6.5 Stochastic Games
6.6 Partially Observable Games
6.7 Limitations of Game Search Algorithms
Part III: Knowledge and Reasoning
7. Logical Agents
7.1 Knowledge-Based Agents
7.2 The Wumpus World
7.3 Logic
7.4 Propositional Logic: A Very Simple Logic
7.5 Propositional Theorem Proving
7.6 Effective Propositional Model Checking
7.7 Agents Based on Propositional Logic
8. First-Order Logic
8.1 Representation Revisited
8.2 Syntax and Semantics of First-Order Logic
8.3 Using First-Order Logic
8.4 Knowledge Engineering in First-Order Logic
9. Inference in First-Order Logic
9.1 Propositional vs.~First-Order Inference
9.2 Unification and First-Order Inference
9.3 Forward Chaining
9.4 Backward Chaining
9.5 Resolution
10. Knowledge Representation
10.1 Ontological Engineering
10.2 Categories and Objects
10.3 Events
10.4 Mental Objects and Modal Logic
10.5 Reasoning Systems for Categories
10.6 Reasoning with Default Information
11. Automated Planning
11.1 Definition of Classical Planning
11.2 Algorithms for Classical Planning
11.3 Heuristics for Planning
11.4 Hierarchical Planning
11.5 Planning and Acting in Nondeterministic Domains
11.6 Time, Schedules, and Resources
11.7 Analysis of Planning Approaches
12. Quantifying Uncertainty
12.1 Acting under Uncertainty
12.2 Basic Probability Notation
12.3 Inference Using Full Joint Distributions
12.4 Independence
12.5 Bayes' Rule and Its Use
12.6 Naive Bayes Models
12.7 The Wumpus World Revisited
Part IV: Uncertain Knowledge and Reasoning
13. Probabilistic Reasoning
13.1 Representing Knowledge in an Uncertain Domain
13.2 The Semantics of Bayesian Networks
13.3 Exact Inference in Bayesian Networks
13.4 Approximate Inference for Bayesian Networks
13.5 Causal Networks
14. Probabilistic Reasoning over Time
14.1 Time and Uncertainty
14.2 Inference in Temporal Models
14.3 Hidden Markov Models
14.4 Kalman Filters
14.5 Dynamic Bayesian Networks
15. Making Simple Decisions
16.1 Combining Beliefs and Desires under Uncertainty
16.2 The Basis of Utility Theory
16.3 Utility Functions
16.4 Multiattribute Utility Functions
16.5 Decision Networks
16.6 The Value of Information
16.7 Unknown Preferences
16. Making Complex Decisions
17.1 Sequential Decision Problems
17.2 Algorithms for MDPs
17.3 Bandit Problems
17.4 Partially Observable MDPs
17.5 Algorithms for solving POMDPs
Part V: Learning
17. Multiagent Decision Making
17.1 Properties of Multiagent Environments
17.2 Non-Cooperative Game Theory
17.3 Cooperative Game Theory
17.4 Making Collective Decisions
18. ProbabilisticProgramming
18.1 Relational Probability Models
18.2 Open-Universe Probability Models
18.3 Keeping Track of a Complex World
18.4 Programs as Probability Models
19. Learning fromExamples
19.1 Forms of Learning
19.2 Supervised Learning
19.3 Learning Decision Trees
19.4 Model Selection and Optimization
19.5 The Theory of Learning
19.6 Linear Regression and Classification
19.7 Nonparametric Models
19.8 Ensemble Learning
19.9 Developing Machine Learning Systems
20. Knowledge inLearning
20.1 A Logical Formulation of Learning
20.2 Knowledge in Learning
20.3 Explanation-Based Learning
20.4 Learning Using Relevance Information
20.5 Inductive Logic Programming
21. LearningProbabilistic Models
21.1 Statistical Learning
21.2 Learning with Complete Data
21.3 Learning with Hidden Variables: The EM Algorithm
22. Deep Learning
22.1 Simple Feedforward Networks
22.2 Mixing and matching models, loss functions andoptimizers
22.3 Loss functions
22.4 Models
22.5 Optimization Algorithms
22.6 Generalization
22.7 Recurrent neural networks
22.8 Unsupervised, semi-supervised and transferlearning
22.9 Applications
Part VI: Communicating, Perceiving, and Acting
23. Reinforcement Learning
23.1 Learning from Rewards
23.2 Passive Reinforcement Learning
23.3 Active Reinforcement Learning
23.4 Safe Exploration
23.5 Generalization in Reinforcement Learning
23.6 Policy Search
23.7 Applications of Reinforcement Learning
24. Natural Language Processing
24.1 Language Models
24.2 Grammar
24.3 Parsing
24.4 Augmented Grammars
24.5 Complications of Real Natural Language
24.6 Natural Language Tasks
25. Deep Learning for Natural Language Processing
25.1 Limitations of Feature-Based NLP Models
25.2 Word Embeddings
25.3 Recurrent Neural Networks
25.4 Sequence-to-sequence Models
25.5 The Transformer Architecture
25.6 Pretraining and Transfer Learning
26. Robotics
26.1 Robots
26.2 Robot Hardware
26.3 What kind of problem is robotics solving?
26.4 Robotic Perception
26.5 Planning and Control
26.6 Planning Uncertain Movements
26.7 Reinforcement Learning in Robotics
26.8 Humans and Robots
26.9 Alternative Robotic Frameworks
26.10 Application Domains
27. Computer Vision
27.1 Introduction
27.2 Image Formation
27.3 Simple Image Features
27.4 Classifying Images
27.5 Detecting Objects
27.6 The 3D World
27.7 Using Computer Vision
Part VII: Conclusions
28. Philosophy and Ethics of AI
28.1 Weak AI: What are the Limits of AI?
28.2 Strong AI: Can Machines Really Think?
28.3 The Ethics of AI
29. The Future of AI
29.1 AI Components
29.2 AI Architectures
Appendix A:Mathematical Background
A.1 Complexity Analysis and O() Notation
A.2 Vectors, Matrices, and Linear Algebra
A.3 Probability Distributions
Appendix B: Notes on Languages and Algorithms
B.1 Defining Languages with Backus--Naur Form (BNF)
B.2 Describing Algorithms with Pseudocode
B.3 Online Supplemental Material