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The first edition of Artificial Intelligence: A Modern Approach has become a classic in the AI literature. It has been adopted by over 600 universities in 60 countries, and has been praised as the definitive synthesis of the field.
In the second edition, every chapter has been extensively rewritten. Significant new material has been introduced to cover areas such as constraint satisfaction, fast propositional inference, planning graphs, internet agents, exact probabilistic inference, Markov Chain Monte Carlo techniques, Kalman filters, ensemble learning methods, statistical learning, probabilistic natural language models, probabilistic robotics, and ethical aspects of AI.
The book is supported by a suite of online resources including source code, figures, lecture slides, a directory of over 800 links to "AI on the Web," and an online discussion group. All of this is available at:
Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the full breadth of the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; and everything from microelectronic devices to robotic planetary explorers. The book is also big because we go into some depth in presenting results, although we strive to cover only the most central ideas in the main part of each chapter. Pointers are given to further results in the bibliographical notes at the end of each chapter.
The subtitle of this book is "A Modern Approach:" The intended meaning of this rather empty phrase is that we have tried to synthesize what is now known into a common framework, rather than trying to explain each subfield of AI in its own historical context. We apologize to those whose subfields are, as a result, less recognizable than they might otherwise have been.
The main unifying theme is the idea of an intelligent agent. We define AI as the study of agents that receive percepts from the environment and perform actions. Each such agent implements a function that maps percept sequences to actions, and we cover different ways to represent these functions, such as production systems, reactive agents, real-time conditional planners, neural networks, and decision-theoretic systems. We explain the role of learning as extending the reach of the designer into unknown environments, and we show how that role constrains agent design, favoring explicit knowledge representation and reasoning. We treat robotics and vision not as independently defined problems, but as occurring in the service of achievinggoals. We stress the importance of the task environment in determining the appropriate agent design.
Our primary aim is to convey the ideas that have emerged over the past fifty years of AI research and the past two millenia of related work. We have tried to avoid excessive formality in the presentation of these ideas while retaining precision. Wherever appropriate, we have included pseudocode algorithms to make the ideas concrete; our pseudocode is described briefly in Appendix B. Implementations in several programming languages are available on the book's Web site, aima.cs.berkeley.edu.
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 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. 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 statistical learning in detail. Some of the required mathematical background is supplied in Appendix A. Overview of the book
The book is divided into eight parts. Part I, Artificial Intelligence, offers a view of the AI enterprise based around the idea of intelligent agents—systems that can decide what to do and then 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 a 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, and what one's actions might do—and how to reason logically with that knowledge. Part IV, Planning, 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-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. 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, analyzes the past and future of AI and the philosophical and ethical implications of artificial intelligence. Changes from the first edition
Much has changed in AI since the publication of the first edition in 1995, and much has changed in this book. Every chapter has been significantly rewritten to reflect the latest work in the field, to reinterpret old work in a way that is more cohesive with new findings, and to improve the pedagogical flow of ideas. Followers of AI should be encouraged that current techniques are much more practical than those of 1995; for example the planning algorithms in the first edition could generate plans of only dozens of steps, while the algorithms in this edition scale up to tens of thousands of steps. Similar orders-of-magnitude improvements are seen in probabilistic inference, language processing, and other subfields. The following are the most notable changes in the book:
The book has 27 chapters, each requiring about a week's worth of lectures, so working through the whole book requires a two-semester sequence. Alternatively, a course can be tailored to suit the interests of the instructor and student. Through its broad coverage, the book can be used to support such courses, whether they are short, introductory undergraduate courses or specialized graduate courses on advanced topics. Sample syllabi from the more than 600 universities and colleges that have adopted the first edition are shown on the Web at aima.cs.berkeley.edu, along with suggestions to help you find a sequence appropriate to your needs.
The book includes 385 exercises. Exercises requiring significant programming are marked with a keyboard icon. These exercises can best be solved by taking advantage of the code repository at aima.cs.berkeley.edu. Some of them are large enough to be considered term projects. A number of exercises require some investigation of the literature; these are marked with a book icon.
Throughout the book, important points are marked with a pointing icon. We have included an extensive index of around 10,000 items to make it easy to find things in the book. Wherever a new term is first defined, it is also marked in the margin. Using the Web site
At the aima.cs.berkeley.edu Web site you will find:
|3||Solving Problems by Searching||59|
|4||Informed Search and Exploration||94|
|5||Constraint Satisfaction Problems||137|
|III||Knowledge and reasoning|
|9||Inference in First-Order Logic||272|
|12||Planning and Acting in the Real World||417|
|V||Uncertain knowledge and reasoning|
|15||Probabilistic Reasoning over Time||537|
|16||Making Simple Decisions||584|
|17||Making Complex Decisions||613|
|18||Learning from Observations||649|
|19||Knowledge in Learning||678|
|20||Statistical Learning Methods||712|
|VII||Communicating, perceiving, and acting|
|23||Probabilistic Language Processing||834|
|27||AI: Present and Future||968|
|B||Notes on Languages and Algorithms||984|
Posted July 28, 2003