|Artificial Intelligence: A Modern Approach|
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 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 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.
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
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 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.
Stuart would like to thank his parents for their continued support and encouragement and his wife, Loy Sheflott, for her endless patience and boundless wisdom. He hopes that Gordon and Lucy will soon be reading this. RUGS (Russell's Unusual Group of Students) have been unusually helpful.
Peter would like to thank his parents (Torsten and Gerda) for getting him started, and his wife (Kris), children, and friends for encouraging and tolerating him through the long hours of writing and longer hours of rewriting.
We are indebted to the librarians at Berkeley, Stanford, MIT, and NASA, and to the developers of CiteSeer and Google, who have revolutionized the way we do research.
We can't thank all the people who have used the book and made suggestions, but we would like to acknowledge the especially helpful comments of Eyal Amir, Krzysztof Apt, Ellery Aziel, Jeff Van Baalen, Brian Baker, Don Barker, Tony Barrett, James Newton Bass, Don Beal, Howard Beck, Wolfgang Bibel, John Binder, Larry Bookman, David R. Boxall, Gerhard Brewka, Selmer Bringsjord, Carla Brodley, Chris Brown, Wilhelm Burger, Lauren Burka, Joao Cachopo, Murray Campbell, Norman Carver, Emmanuel Castro, Anil Chakravarthy, Dan Chisarick, Roberto Cipolla, David Cohen, James Coleman, Julie Ann Comparini, Gary Cottrell, Ernest Davis, Rina Dechter, Tom Dietterich, Chuck Dyer, Barbara Engelhardt, Doug Edwards, Kutluhan Erol, Oren Etzioni, Hana Filip, Douglas Fisher, Jeffrey Forbes, Ken Ford, John Fosler, Alex Franz, Bob Futrelle, Marek Galecki, Stefan Gerberding, Stuart Gill, Sabine Glesner, Seth Golub, Gosta Grahne, Russ Greiner, Eric Grimson, Barbara Grosz, Larry Hall, Steve Hanks, Othar Hansson, Ernst Heinz, Jim Hendler, Christoph Herrmann, Vasant Honavar, Tim Huang, Seth Hutchinson, Joost Jacob, Magnus Johansson, Dan Jurafsky, Leslie Kaelbling, Keiji Kanazawa, Surekha Kasibhatla, Simon Kasif, Henry Kautz, Gernot Kerschbaumer, Richard Kirby, Kevin Knight, Sven Koenig, Daphne Koller, Rich Korf, James Kurien, John Lafferty, Gus Larsson, John Lazzaro, Jon LeBlanc, Jason Leatherman, Frank Lee, Edward Lim, Pierre Louveaux, Don Loveland, Sridhar Mahadevan, Jim Martin, Andy Mayer, David McGrane, Jay Mendelsohn, Brian Milch, Steve Minton, Vibhu Mittal, Leora Morgenstern, Stephen Muggleton, Kevin Murphy, Ron Musick, Sung Myaeng, Lee Naish, Pandu Nayak, Bernhard Nebel, Stuart Nelson, XuanLong Nguyen, Illah Nourbakhsh, Steve Omohundro, David Page, David Palmer, David Parkes, Ron Parr, Mark Paskin, Tony Passera, Michael Pazzani, Wim Pijls, Ira Pohl, Martha Pollack, David Poole, Bruce Porter, Malcolm Pradhan, Bill Pringle, Lorraine Prior, Greg Provan, William Rapaport, Philip Resnik, Francesca Rossi, Jonathan Schaeffer, Richard Scherl, Lars Schuster, Soheil Shams, Stuart Shapiro, Jude Shavlik, Satinder Singh, Daniel Sleator, David Smith, Bryan So, Robert Sproull, Lynn Stein, Larry Stephens, Andreas Stolcke, Paul Stradling, Devika Subramanian, Rich Sutton, Jonathan Tash, Austin Tate, Michael Thielscher, William Thompson, Sebastian Thrun, Eric Tiedemann, Mark Torrance, Randall Upham, Paul Utgoff, Peter van Beek, Hal Varian, Sunil Vemuri, Jim Waldo, Bonnie Webber, Dan Weld, Michael Wellman, Michael Dean White, Kamin Whitehouse, Brian Williams, David Wolfe, Bill Woods, Alden Wright, Richard Yen, Weixiong Zhang, Shlomo Zilberstein, and the anonymous reviewers provided by Prentice Hall.
|AI: A Modern Approach by Stuart Russell and Peter Norvig||Modified: Jun 24, 2003|