Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
Jennifer Widom. Add Comment. Save my name, email, and website in this browser for the next time I comment. Post Comment. Chapter 2 provides an overview of agent design, including a basic agent and environment vii Preface Vlll project. Algorithms are presented at three levels of detail: prose descriptions and! All the agent programs are interoperable and work in a uniform framework for simulated environments. This book is primarily intended for use in an undergraduate course or course sequence.
It can also be used in a graduate-level course perhaps with the addition of some of the primary sources suggested in the bibliographical notes. Because of its comprehensive coverage and the large number of detailed algorithms, it is useful as a primary reference volume for AI graduate students and professionals wishing to branch out beyond their own subfield.
We also hope that AI researchers could benefit from thinking about the unifying approach we advocate. The only prerequisite is familiarity with basic concepts of computer science algorithms, data structures, complexity at a sophomore level. Freshman calculus is useful for understanding neural networks and adaptive probabilistic networks in detail. Some experience with nonnumeric programming is desirable, but can be picked up in a few weeks study. Overview of the book The book is divided into eight parts.
Part 1, "Artificial Intelligence," sets the stage for all the others, and offers a view of the AI enterprise based around the idea of intelligent agents—systems that can decide what to do and do it.
Part II, "Problem Solving," concentrates on methods for deciding what to do when one needs to think ahead several steps, for example in navigating across country or playing chess. Part III, "Knowledge and Reasoning," discusses ways to represent knowledge about the world—how it works, what it is currently like, what one's actions might do—and how to reason logically with that knowledge.
Part IV, "Acting Logically," then discusses how to use these reasoning methods to decide what to do, particularly by constructing plans. Part V, "Uncertain Knowledge and Reasoning," is analogous to Parts III and IV, but it concentrates on reasoning and decision-making in the presence of uncertainty about the world, as might be faced, for example, by a system for medical diagnosis and treatment.
Together, Parts II to V describe that part of the intelligent agent responsible for reaching decisions. Part VI, "Learning," describes methods for generating the knowledge required by these decision-making components; it also introduces a new kind of component, the neural network, and its associated learning procedures. Part VII, "Communicating, Perceiving, and Acting," describes ways in which an intelligent agent can perceive its environment so as to know what is going on, whether by vision, touch, hearing, or understanding language; and ways in which it can turn its plans into real actions, either as robot motion or as natural language utterances.
Finally, Part VIII, "Conclusions," analyses the past and future of AI, and provides some light amusement by discussing what AI really is and why it has already succeeded to some degree, and airing the views of those philosophers who believe that AI can never succeed at all. Preface Using this book This is a big book; covering all the chapters and the projects would take two semesters.
You will notice that the book is divided into 27 chapters, which makes it easy to select the appropriate material for any chosen course of study. Each chapter can be covered in approximately one week. These sequences could be used for both undergraduate and graduate courses. The relevant parts of the book could also be used to provide the first phase of graduate specialty courses.
For example, Part VI could be used in conjunction with readings from the literature in a course on machine learning. We have decided not to designate certain sections as "optional" or certain exercises as "difficult," as individual tastes and backgrounds vary widely.
Exercises requiring significant programming are marked with a keyboard icon, and those requiring some investigation of the literature are marked with a book icon. Altogether, over exercises are included. Some of them are large enough to be considered term projects. Many of the exercises can best be solved by taking advantage of the code repository, which is described in Appendix B.
Throughout the book, important points are marked with a pointing icon. If you have any comments on the book, we'd like to hear from you. Appendix B includes information on how to contact us. Doug Edwards researched the Historical Notes sections for all chapters and wrote much of them. Tim Huang helped with formatting of the diagrams and algorithms.
Maryann Simmons prepared the 3-D model from which the cover illustration was produced, and Lisa Marie Sardegna did the postprocessing for the final image. Alan Apt, Mona Pompili, and Sondra Chavez at Prentice Hall tried their best to keep us on schedule and made many helpful suggestions on design and content. Preface Stuart would like to thank his parents, brother, and sister for their encouragement and their patience at his extended absence.
He hopes to be home for Christmas. He would also like to thank Loy Sheflott for her patience and support. He hopes to be home some time tomorrow afternoon. His intellectual debt to his Ph. Peter would like to thank his parents Torsten and Gerda for getting him started, his advisor Bob Wilensky , supervisors Bill Woods and Bob Sproull and employer Sun Microsystems for supporting his work in AI, and his wife Kris and friends for encouraging and tolerating him through the long hours of writing.
Before publication, drafts of this book were used in 26 courses by about students. Both of us deeply appreciate the many comments of these students and instructors and other reviewers. Summary of Contents i Artificial Intelligence ii 1 1 Introduction Acting humanly: The Turing Test approach. Thinking humanly: The cognitive modelling approach.
Thinking rationally: The laws of thought approach. Acting rationally: The rational agent approach. Philosophy B. Mathematics c. Psychology present. Computer engineering present. Linguistics present. The gestation of artificial intelligence Early enthusiasm, great expectations A dose of reality Knowledge-based systems: The key to power? AI becomes an industry The return of neural networks present.
Recent events present. Bibliographical and Historical Notes. The ideal mapping from percept sequences to actions Autonomy. Agent programs. Why not just look up the answers? An example. Simple reflex agents. Goal-based agents. Utility-based agents. Contents XIV Properties of environments. Environment programs. Knowledge and problem types. Well-defined problems and solutions. Measuring problem-solving performance.
Choosing states and actions. Toy problems. Real-world problems. Generating action sequences. Data structures for search trees. Breadth-first search. Uniform cost search. Depth-first search. Depth-limited search. Iterative deepening search. Bidirectional search. Comparing search strategies. Specifying the environment. Acting and reasoning in the wumpus world 6.
Canonical form. Sample proof revisited. Resolution: A Complete Inference Procedure. The resolution inference rule. Canonical forms for resolution. Resolution proofs. Conversion to Normal Form. Example proof. Speech and Language Processing, 2nd ed.
Learning Bayesian Networks. Artificial Intelligence: A Modern Approach, 3rd. Russell and Peter Norvig. It was first published in and the third edition of the book was released 11 December It is used in over universities worldwide [1] and has been called 'the most popular artificial intelligence textbook in the world'. The book is intended for an undergraduate audience but can also be used for graduate-level studies with the suggestion of adding some of the primary sources listed in the extensive bibliography.
Artificial Intelligence: A Modern Approach is divided into seven parts with a total of 27 chapters.
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