# Preface

In the years since the first edition of this book appeared, Artificial Intelligence (AI) has grown from a small-scale laboratory science into a technological and industrial success. We now possess an arsenal of techniques for creating computer programs that control manufacturing processes, diagnose computer faults and human diseases, design computers, do insurance underwriting, play grandmaster-level chess, and so on. Basic research in AI has expanded enormously during this period. For the student, extracting theoretical and practical knowledge from such a large body of scientific knowledge is a daunting task. The goal of the first edition of this book was to provide a readable introduction to the problems and techniques of AI. In this edition, we have tried to achieve the same goal for the expanded field that AI has become. In particular, we have tried to present both the theoretical foundations of AI and an indication of the ways that current techniques can be used in application programs.

As a result of this effort, the book has grown. It is probably no longer possible to cover everything in a single semester. Because of this, we have structured the book so that an instructor can choose from a variety of paths through the chapters. The book is divided into three parts:

Part I. Problems and Search.

Part II. Knowledge Representation.

Part III. Advanced Topics.

Part I introduces AI by examining the nature of the difficult problems that AI seeks to solve. It then develops the theory and practice of heuristic search, providing detailed algorithms for standard search methods, including best-first search, hill climbing, simulated annealing, means-ends analysis, and constraint satisfaction.

The last thirty years of AI have demonstrated that intelligence requires more than the ability to reason. It also requires a great deal of knowledge about the world. So Part II explores a variety of methods for encoding knowledge in computer systems. These methods include predicate logic, production rules, semantic networks, frames, and scripts. There are also chapters on both symbolic and numeric techniques for reasoning under uncertainty. In addition, we present some very specific frameworks in which particular commitments to a set of representational primitives are made.

Parts I and II should be covered in any basic course in AI. They provide the foundation for the advanced topics and applications that are presented in Part III. While the chapters in Parts I and II should be covered in order since they build on each other, the chapters in Part III are, for the most part, independent and can be covered in almost any combination, depending on the goals of a particular course. The topics that are covered include: game playing, planning, understanding, natural language processing (which depends on the understanding chapter), parallel and distributed AI (which depends on planning and natural language), learning, connectionist models, common sense, expert systems, and perception and action.

To use this book effectively, students should have some background in both computer science and mathematics. As computer science background, they should have experience programming and they should feel comfortable with the material in an undergraduate data structures course. They should be familiar with the use of recursion as a program control structure. And they should be able to do simple analyses of the time complexity of algorithms. As mathematical background, students should have the equivalent of an undergraduate course in logic, including predicate logic with quantifiers and the basic notion of a decision procedure.

This book contains, spread throughout it, many references to the AI research literature. These references are important for two reasons. First, they make it possible for the student to pursue individual topics in greater depth than is possible within the space restrictions of this book. This is the common reason for ineluding references in a survey text. The second reason that these references have been ineluded is more specific to the content of this book. AI is a relatively new discipline. In many areas of the field there is still not complete agreement on how things should be done. The references to the source literature guarantee that students have access not just to one approach, but to as many as possible of those whose eventual success still needs to be determined by further researeh, both theoretical and empirical.

Since the ultimate goal of AI is the construction ofprograms that solve hard problems, no study of AI is complete without some experience writing programs. Most AI programs are written in LISP, PROLOG, or some specialized AI shell. Recently though, as AI has spread out into the mainstream computing world, AI programs are being written in a wide variety of programming languages. The algorithms presented in this book are deseribed in sufficient detail to enable students to exploit them in their programs, but they are not expressed in code. This book should probably be supplemented with a good book on whatever language is being used for programming in the course.

This book would not have happened without the help of many people. The content of the manuseript has been greatly improved by the comments of Srinivas Akella, Jim Blevins, Clay Bridges, R. Martin Chavez, Alan Cline, Adam Farquar, Anwar Ghuloum, Yolanda Gil, R. V. Guha, Lucy Hadden, Ajay Jain, Craig Knoblock, John Laird, Clifford Mercer, Michael Newton, Charles Petrie, Robert Rich, Steve Shafer, Reid Simmons, Herbert Simon, Munindar Singh, Milind Tambe, David Touretzky, Martuela Veloso, David Wroblewski, and Marco Zagha.

Special thanks to Yolanda Gil and Alan Cline for help above and beyond. Yolanda kept the project going under desperate circumstances, and Alan spent innumerable hours designing the cover and bringing it into the world. We thank them for these things and much, much more.

Linda Mitchell helped us put together many draft editions along the way. Some of those drafts were used in actual courses, where students found innumerable bugs for us. We would like to thank them as well as their instructors, Tom Mitehell and Jean Scholtz. Thanks also to Don Speray for his help in producing the cover.

David Shapiro and Joe Murphy deserve credit for superb editing, and for keeping us on sehedule.

We would also like to thank Nicole Vecchi for her wisdom and patience in the world of high resolution printing. Thanks to David Long and Lily Mummert for pointing us to the right fonts.

Thanks to the following reviewers for their comments: Yigal Arens, University of Southern California; Jaime Carbonell, Camegie Mellon University; Charles Dyer, University of Wisconsin, Madison; George Ernst, Case Western Reserve University; Pat Langley, University of California, Irvine; Brian Sehunek, University of Michigan; and James Slagle, University of Minnesota.

Camegie Mellon University and MCC prrvided us the environment in which we could write and produce this book. We would like to thank our colleagues, particularly Jim Barnett and Masaru Tomita, for putting up with us while we were writing this book instead of doing the other things we were supposed to be doing.

Elaine Rich

Keivin Knight