Artificial Intelligence: A Modern Approach, Global Edition eBook (3e) : 9781292153971

Artificial Intelligence: A Modern Approach, Global Edition eBook (3e)

 
Edition
 
3
ISBN
 
9781292153971
ISBN 10
 
1292153970
Published
 
22/06/2016
Published by
 
Pearson Higher Ed USA
Pages
 
Format
 
 
Title type
eBook
$65.00
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About the book

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. 

Table of contents
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 Summary, Bibliographical and Historical Notes, Exercises

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

2.5 Summary, Bibliographical and Historical Notes, Exercises

II. Problem-solving

3. Solving Problems by Searching

3.1 Problem-Solving Agents

3.2 Example Problems

3.3 Searching for Solutions

3.4 Uninformed Search Strategies

3.5 Informed (Heuristic) Search Strategies

3.6 Heuristic Functions

3.7 Summary, Bibliographical and Historical Notes, Exercises

4. Beyond Classical Search

4.1 Local Search Algorithms and Optimization Problems

4.2 Local Search in Continuous Spaces

4.3 Searching with Nondeterministic Actions

4.4 Searching with Partial Observations

4.5 Online Search Agents and Unknown Environments

4.6 Summary, Bibliographical and Historical Notes, Exercises

5. Adversarial Search

5.1 Games

5.2 Optimal Decisions in Games

5.3 Alpha—Beta Pruning

5.4 Imperfect Real-Time Decisions

5.5 Stochastic Games

5.6 Partially Observable Games

5.7 State-of-the-Art Game Programs

5.8 Alternative Approaches

5.9 Summary, Bibliographical and Historical Notes, Exercises

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

6.6 Summary, Bibliographical and Historical Notes, Exercises

III. Knowledge, Reasoning, and Planning

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

7.8 Summary, Bibliographical and Historical Notes, Exercises

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

8.5 Summary, Bibliographical and Historical Notes, Exercises

9. Inference in First-Order Logic

9.1 Propositional vs. First-Order Inference

9.2 Unification and Lifting

9.3 Forward Chaining

9.4 Backward Chaining

9.5 Resolution

9.6 Summary, Bibliographical and Historical Notes, Exercises

10. Classical Planning

10.1 Definition of Classical Planning

10.2 Algorithms for Planning as State-Space Search

10.3 Planning Graphs

10.4 Other Classical Planning Approaches

10.5 Analysis of Planning Approaches

10.6 Summary, Bibliographical and Historical Notes, Exercises

11. Planning and Acting in the Real World

11.1 Time, Schedules, and Resources

11.2 Hierarchical Planning

11.3 Planning and Acting in Nondeterministic Domains

11.4 Multiagent Planning

11.5 Summary, Bibliographical and Historical Notes, Exercises

12 Knowledge Representation

12.1 Ontological Engineering

12.2 Categories and Objects

12.3 Events

12.4 Mental Events and Mental Objects

12.5 Reasoning Systems for Categories

12.6 Reasoning with Default Information

12.7 The Internet Shopping World

12.8 Summary, Bibliographical and Historical Notes, Exercises

IV. Uncertain Knowledge and Reasoning

13. Quantifying Uncertainty

13.1 Acting under Uncertainty

13.2 Basic Probability Notation

13.3 Inference Using Full Joint Distributions

13.4 Independence

13.5 Bayes’ Rule and Its Use

13.6 The Wumpus World Revisited

13.7 Summary, Bibliographical and Historical Notes, Exercises

14. Probabilistic Reasoning

14.1 Representing Knowledge in an Uncertain Domain

14.2 The Semantics of Bayesian Networks

14.3 Efficient Representation of Conditional Distributions

14.4 Exact Inference in Bayesian Networks

14.5 Approximate Inference in Bayesian Networks

14.6 Relational and First-Order Probability Models

14.7 Other Approaches to Uncertain Reasoning

14.8 Summary, Bibliographical and Historical Notes, Exercises

15. Probabilistic Reasoning over Time

15.1 Time and Uncertainty

15.2 Inference in Temporal Models

15.3 Hidden Markov Models

15.4 Kalman Filters

15.5 Dynamic Bayesian Networks

15.6 Keeping Track of Many Objects

15.7 Summary, Bibliographical and Historical Notes, Exercises

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 Decision-Theoretic Expert Systems

16.8 Summary, Bibliographical and Historical Notes, Exercises

17. Making Complex Decisions

17.1 Sequential Decision Problems

17.2 Value Iteration

17.3 Policy Iteration

17.4 Partially Observable MDPs

17.5 Decisions with Multiple Agents: Game Theory

17.6 Mechanism Design

17.7 Summary, Bibliographical and Historical Notes, Exercises

V. Learning

18. Learning from Examples

18.1 Forms of Learning

18.2 Supervised Learning

18.3 Learning Decision Trees

18.4 Evaluating and Choosing the Best Hypothesis

18.5 The Theory of Learning

18.6 Regression and Classification with Linear Models

18.7 Artificial Neural Networks

18.8 Nonparametric Models

18.9 Support Vector Machines

18.10 Ensemble Learning

18.11 Practical Machine Learning

18.12 Summary, Bibliographical and Historical Notes, Exercises

19. Knowledge in Learning

19.1 A Logical Formulation of Learning

19.2 Knowledge in Learning

19.3 Explanation-Based Learning

19.4 Learning Using Relevance Information

19.5 Inductive Logic Programming

19.6 Summary, Bibliographical and Historical Notes, Exercises

20. Learning Probabilistic Models

20.1 Statistical Learning

20.2 Learning with Complete Data

20.3 Learning with Hidden Variables: The EM Algorithm

20.4 Summary, Bibliographical and Historical Notes, Exercises

21. Reinforcement Learning

21.1 Introduction

21.2 Passive Reinforcement Learning

21.3 Active Reinforcement Learning

21.4 Generalization in Reinforcement Learning

21.5 Policy Search

21.6 Applications of Reinforcement Learning

21.7 Summary, Bibliographical and Historical Notes, Exercises

VI. Communicating, Perceiving, and Acting

22. Natural Language Processing

22.1 Language Models

22.2 Text Classification

22.3 Information Retrieval

22.4 Information Extraction

22.5 Summary, Bibliographical and Historical Notes, Exercises

23. Natural Language for Communication

23.1 Phrase Structure Grammars

23.2 Syntactic Analysis (Parsing)

23.3 Augmented Grammars and Semantic Interpretation

23.4 Machine Translation

23.5 Speech Recognition

23.6 Summary, Bibliographical and Historical Notes, Exercises

24. Perception

24.1 Image Formation

24.2 Early Image-Processing Operations

24.3 Object Recognition by Appearance

24.4 Reconstructing the 3D World

24.5 Object Recognition from Structural Information

24.6 Using Vision

24.7 Summary, Bibliographical and Historical Notes, Exercises

25. Robotics

25.1 Introduction

25.2 Robot Hardware

25.3 Robotic Perception

25.4 Planning to Move

25.5 Planning Uncertain Movements

25.6 Moving

25.7 Robotic Software Architectures

25.8 Application Domains

25.9 Summary, Bibliographical and Historical Notes, Exercises

VII. Conclusions

26 Philosophical Foundations

26.1 Weak AI: Can Machines Act Intelligently?

26.2 Strong AI: Can Machines Really Think?

26.3 The Ethics and Risks of Developing Artificial Intelligence

26.4 Summary, Bibliographical and Historical Notes, Exercises

27. AI: The Present and Future

27.1 Agent Components

27.2 Agent Architectures

27.3 Are We Going in the Right Direction?

27.4 What If AI Does Succeed?

Appendices

A. Mathematical Background

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 Help

Bibliography

Index
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