Artificial Intelligence

Description

Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.

Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence.

According to an article in The New York Times, the course on artificial intelligence is “one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world.” One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. Jennifer Widom.

Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your KindleTM, NOOKTM, and the iPhone®/iPad®.

To learn more about the course on artificial intelligence, visit http://www.ai-class.com. To read the full New York Times article, click here.

This edition captures the changes in AI that have taken place since the last edition in 2003. There have been important applications of AI technology, such as the widespread deployment of practical speech recognition, machine translation, autonomous vehicles, and household robotics. There have been algorithmic landmarks, such as the solution of the game of checkers. And there has been a great deal of theoretical progress, particularly in areas such as probabilistic reasoning, machine learning, and computer vision. Most important from the authors’ point of view is the continued evolution in how we think about the field, and thus how the book is organized. The major changes are as follows:

• More emphasis is placed on partially observable and nondeterministic environments, especially in the nonprobabilistic settings of search and planning. The concepts of belief state (a set of possible worlds) and state estimation (maintaining the belief state) are introduced in these settings; later in the book, probabilities are added.
• In addition to discussing the types of environments and types of agents, there is more in more depth coverage of the types of representations that an agent can use. Differences between atomic representations (in which each state of the world is treated as a black box), factored representations (in which a state is a set of attribute/value pairs), and structured representations (in which the world consists of objects and relations between them) are distinguished.
• Coverage of planning goes into more depth on contingent planning in partially observable environments and includes a new approach to hierarchical planning.
• New material on first-order probabilistic models is added, including open-universe models for cases where there is uncertainty as to what objects exist.
• The introductory machine-learning chapter is completely rewritten, stressing a wider variety of more modern learning algorithms and placing them on a firmer theoretical footing.
• Expanded coverage of Web search and information extraction, and of techniques for learning from very large data sets.
• 20% of the citations in this edition are to works published after 2003.
• Approximately 20% of the material is brand new. The remaining 80% reflects older work but is largely rewritten to present a more unified picture of the field.

Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle™, NOOK™, and the iPhone®/iPad®.

You can also purchase the eText for 180 days through CourseSmart http://www.mypearsonstore.com/bookstore/product.asp?isbn=0136067336

• Nontechnical learning material.

  • Provides a simple overview of major concepts, uses a nontechnical language to help increase understanding. Makes the book accessible to a broader range of students.

• The Internet as a sample application for intelligent systems — Examples of logical reasoning, planning, and natural language processing using Internet agents.

  • Promotes student interest with interesting, relevant exercises.

• Increased coverage of material — New or expanded coverage of constraint satisfaction, local search planning methods, multi-agent systems, game theory, statistical natural language processing and uncertain reasoning over time. More detailed descriptions of algorithms for probabilistic inference, fast propositional inference, probabilistic learning approaches including EM, and other topics.

  • Brings students up to date on the latest technologies, and presents concepts in a more unified manner.

• Updated and expanded exercises — 30% of the exercises are revised or NEW.

• More Online Software.

  • Allows many more opportunities for student projects on the web.

• A unified, agent-based approach to AI — Organizes the material around the task of building intelligent agents.

  • Shows students how the various subfields of AI fit together to build actual, useful programs.

• Comprehensive, up-to-date coverage — Includes a unified view of the field organized around the rational decision making paradigm.

• A flexible format.

  • Makes the text adaptable for varying instructors’ preferences.

• In-depth coverage of basic and advanced topics.

  • Provides students with a basic understanding of the frontiers of AI without compromising complexity and depth.

• Pseudo-code versions of the major AI algorithms are presented in a uniform fashion, and Actual Common Lisp and Python implementations of the presented algorithms are available via the Internet.

  • Gives instructors and students a choice of projects; reading and running the code increases understanding.

• Author Maintained Website

  • Visit http://aima.cs.berkeley.edu/ to access text-related Comments and Discussions, AI Resources on the Web, and Online Code Repository, Instructor Resources, and more!

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 of computer science, director of the Center for Intelligent Systems, 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 cowinner of the Computers and Thought Award. He was a 1996 Miller Professor of the University of California and was appointed to a Chancellor’s Professorship in 2000. In 1998, he gave the Forsythe Memorial Lectures at Stanford University. He is a Fellow and former Executive Council member of the American Association for Artificial Intelligence. He has published over 100 papers on a wide range of topics in artificial intelligence. His other books include The Use of Knowledge in Analogy and Induction and (with Eric Wefald) Do the Right Thing: Studies in Limited Rationality.

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 a research faculty member at Berkeley. His other books are Paradigms of AI Programming: Case Studies in Common Lisp and Verbmobil: A Translation System for Faceto-Face Dialog and Intelligent Help Systems for UNIX.

View chapters 3 and 4 from the Third Edition.

Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle™, NOOK™, and the iPhone®/iPad®.

You can also purchase the eText for 180 days through CourseSmart http://www.mypearsonstore.com/bookstore/product.asp?isbn=0136067336

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

Additional information

Dimensions	2.05 × 9.20 × 11.10 in
Imprint	Pearson
Format	gen
ISBN-13	09780136042594
ISBN-10	0136042597
Author	Peter Norvig, Stuart Russell
Subjects	artificial intelligence, computer science, higher education, Engineering and Computer Science