Home > Store

Artificial Intelligence: A Modern Approach, 3rd Edition

Register your product to gain access to bonus material or receive a coupon.

Artificial Intelligence: A Modern Approach, 3rd Edition


  • Your Price: $192.65
  • List Price: $226.65
  • Usually ships in 24 hours.



  • 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!


  • Copyright 2010
  • Dimensions: 8" x 10"
  • Pages: 1152
  • Edition: 3rd
  • Book
  • ISBN-10: 0-13-604259-7
  • ISBN-13: 978-0-13-604259-4

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.

Sample Content

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?


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




Submit Errata

More Information

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information

To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.


Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.


If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information

Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.


This site is not directed to children under the age of 13.


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information

If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information

Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents

California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure

Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact

Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice

We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020