Artificial intelligence : a modern approach / Stuart J. Russell and Peter Norvig ; contributing writers: Ming-Wei Chang, Jacob Devlin, Anca Dragan, David Forsyth, Ian Goodfellow, Jitenda M. Malik, Vikash Mansinghka, Judea Pearl, Michael Wooldridge.

Format:

Book

Author/Creator:

Russell, Stuart J. (Stuart Jonathan), 1962- author.

Norvig, Peter, author.

Contributor:

Chang, Ming-Wei, contributor.

Devlin, Jacob (Engineer), contributor.

Dragan, Anca, contributor.

Forsyth, David, contributor.

Goodfellow, Ian, contributor.

Malik, Jitendra, contributor.

Mansinghka, Vikash, contributor.

Pearl, Judea, contributor.

Wooldridge, Michael J., 1966- contributor.

Engineering Book Fund.

Series:

Pearson series in artificial intelligence.

Pearson series in artificial intelligence

Language:

English

Subjects (All):

Artificial intelligence.

Artificial Intelligence.

artificial intelligence.

Medical Subjects:

Artificial Intelligence.

Genre:

Textbooks

Textbooks.

Physical Description:

xvii, 1115 pages : illustrations (some color) ; 27 cm.

Edition:

Fourth edition.

Place of Publication:

Hoboken, NJ : Pearson, [2021]

Summary:

"Updated edition of popular textbook on Artificial Intelligence. This edition specific looks at ways of keeping artificial intelligence under control" -- Provided by publisher.

"Updated edition of popular textbook on Artificial Intelligence. This edition specific looks at ways of keeping artificial intelligence under control"-- Provided by publisher.

Contents:

I. 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. Conclusion; 27. Philosophy and Ethics of AI ; 28. The Future of AI

A. Mathematical Background

B. Notes on Language and Algorithms.

Machine generated contents note: I. Artificial Intelligence

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

Summary

Bibliographical and Historical Notes

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

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. Adversarial Search and Games

5.1. Game Theory

5.2. Optimal Decisions in Games

5.3. Heuristic Alpha

Beta Tree Search

5.4. Monte Carlo Tree Search

5.5. Stochastic Games

5.6. Partially Observable Games

5.7. Limitations of Game Search Algorithms

6. Constraint Satisfaction Problems

6.1. Defining Constraint Satisfaction Problems

6.2. Constraint Propagation: Inference in CSPs

6.3. Backtracking Search for CSPs

6.4. Local Search for CSPs

6.5. The Structure of Problems

III. Knowledge, reasoning, and planning

7. Logical Agents

7.1. Knowledge-Based Agents

7.2. The Wumpus World

7.3. Logic

7.4. Prepositional Logic: A Very Simple Logic

7.5. Prepositional Theorem Proving

7.6. Effective Prepositional Model Checking

7.7. Agents Based on Prepositional 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. Prepositional 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

IV. Uncertain knowledge and reasoning

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

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. Probabilistic Programming

15.1. Relational Probability Models

15.2. Open-Universe Probability Models

15.3. Keeping Track of a Complex World

15.4. Programs as Probability Models

16. 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

17. 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

18. Multiagent Decision Making

18.1. Properties of Multiagent Environments

18.2. Non-Cooperative Game Theory

18.3. Cooperative Game Theory

18.4. Making Collective Decisions

V. Machine Learning

19. Learning from Examples

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. Learning Probabilistic Models

20.1. Statistical Learning

20.2. Learning with Complete Data

20.3. Learning with Hidden Variables: The EM Algorithm

21. Deep Learning

21.1. Simple Feedforward Networks

21.2. Computation Graphs for Deep Learning

21.3. Convolutional Networks

21.4. Learning Algorithms

21.5. Generalization

21.6. Recurrent Neural Networks

21.7. Unsupervised Learning and Transfer Learning

21.8. Applications

22. Reinforcement Learning

22.1. Learning from Rewards

22.2. Passive Reinforcement Learning

22.3. Active Reinforcement Learning

22.4. Generalization in Reinforcement Learning

22.5. Policy Search

22.6. Apprenticeship and Inverse Reinforcement Learning

22.7. Applications of Reinforcement Learning

VI. Communicating, perceiving, and acting

23. Natural Language Processing

23.1. Language Models

23.2. Grammar

23.3. Parsing

23.4. Augmented Grammars

23.5. Complications of Real Natural Language

23.6. Natural Language Tasks

24. Deep Learning for Natural Language Processing

24.1. Word Embeddings

24.2. Recurrent Neural Networks for NLP

24.3. Sequence-to-Sequence Models

24.4. The Transformer Architecture

24.5. Pretraining and Transfer Learning

24.6. State of the art

25. Computer Vision

25.1. Introduction

25.2. Image Formation

25.3. Simple Image Features

25.4. Classifying Images

25.5. Detecting Objects

25.6. The 3D World

25.7. Using Computer Vision

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

VII. Conclusions

27. Philosophy, Ethics, and Safety of AI

27.1. The Limits of AI

27.2. Can Machines Really Think?

27.3. The Ethics of AI

28. The Future of AI

28.1. AI Components

28.2. AI Architectures

A.1. Complexity Analysis and O() Notation

A.2. Vectors, Matrices, and Linear Algebra

A.3. Probability Distributions

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.

Notes:

Includes bibliographical references (pages 1033-1067) and index.

Local Notes:

Acquired for the Penn Libraries with assistance from the Engineering Book Fund.

Other Format:

Online version: Russell, Stuart J. (Stuart Jonathan) Artificial intelligence.

ISBN:

9780134610993

0134610997

OCLC:

1124776132

Publisher Number:

99987490847