Artificial Intelligence: A Modern Approach 4th Edition
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; fairness, trust, social good, and safety; and applications that range from microelectronic devices to robotic planetary explorers to online services with billions of users.
The subtitle of this book is “A Modern Approach.” That means we have chosen to tell the story from a current perspective. We synthesize what is now known into a common framework, recasting early work using the ideas and terminology that are prevalent today. We apologize to those whose subfields are, as a result, less recognizable.
New to this edition
This edition reflects the changes in AI since the last edition in 2010:
We focus more on machine learning rather than hand-crafted knowledge engineering, due to the increased availability of data, computing resources, and new algorithms. Deep learning, probabilistic programming, and multiagent systems receive expanded coverage, each with their own chapter.
The coverage of natural language understanding, robotics, and computer vision has been revised to reflect the impact of deep learning.
The robotics chapter now includes robots that interact with humans and the application of reinforcement learning to robotics.
Previously we defined the goal of AI as creating systems that try to maximize expected utility, where the specific utility information—the objective—is supplied by the human designers of the system. Now we no longer assume that the objective is fixed and known by the AI system; instead, the system may be uncertain about the true objectives of the humans on whose behalf it operates. It must learn what to maximize and must function appropriately even while uncertain about the objective.
We increase coverage of the impact of AI on society, including the vital issues of ethics, fairness, trust, and safety.
We have moved the exercises from the end of each chapter to an online site. This allows us to continuously add to, update, and improve the exercises, to meet the needs of instructors and to reflect advances in the field and in AI-related software tools. Overall, about 25% of the material in the book is brand new. The remaining 75% has been largely rewritten to present a more unified picture of the field. 22% of the citations in this edition are to works published after 2010.
Overview of the book
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 reactive agents, real-time planners, decision-theoretic systems, and deep learning systems. We emphasize learning both as a construction method for competent systems and as a way of extending the reach of the designer into unknown environments. We treat robotics and vision not as independently defined problems, but as occurring in the service of achieving goals. 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 seventy years of AI research and the past two millennia of related work. We have tried to avoid excessive formality in the presentation of these ideas, while retaining precision. We have included mathematical formulas and pseudocode algorithms to make the key ideas concrete; mathematical concepts and notation are described in Appendix A and our pseudocode is described in Appendix B.
This book is primarily intended for use in an undergraduate course or course sequence. The book has 28 chapters, each requiring about a week’s worth of lectures, so working through the whole book requires a two-semester sequence. A one-semester course can use selected chapters to suit the interests of the instructor and students. The book can also be used in a graduate-level course (perhaps with the addition of some of the primary sources suggested in the bibliographical notes), or for self-study or as a reference.
Throughout the book, important points are marked with a triangle icon in the margin. Wherever a new term is defined, it is also noted in the margin. Subsequent significant uses of the term are in bold, but not in the margin. We have included a comprehensive index and an extensive bibliography.
The only prerequisite is familiarity with basic concepts of computer science (algorithms, data structures, complexity) at a sophomore level. Freshman calculus and linear algebra are useful for some of the topics.
Online resources are available through pearsonhighered.com/cs-resources or at the book’s Web site, aima.cs.berkeley.edu. There you will find:
Exercises, programming projects, and research projects. These are no longer at the end of each chapter; they are online only. Within the book, we refer to an online exercise with a name like “Exercise 6.NARY.” Instructions on the Web site allow you to find exercises by name or by topic.
Implementations of the algorithms in the book in Python, Java, and other programming
languages (currently hosted at github.com/aimacode).
A list of over 1400 schools that have used the book, many with links to online course materials and syllabi.
Supplementary material and links for students and instructors.
Instructions on how to report errors in the book, in the likely event that some exist.
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|January 19, 2022|
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