We describe our experiences in teaching introductory AI and in writing a textbook for the course. The book tries to make the concepts of AI more concrete via two strategies: relating them to the student's existing knowledge, and using examples based on an agent operating in an environment.
Perceived Problems with Current AI TextsIn the dozen or so times we have taught introductory AI, we have used several of the existing texts, and have always had complaints from students. In a recent student evaluation survey at Berkeley, the text for the AI course was ranked lowest of all texts in computer science courses. Other instructors we have talked to share this sentiment. Some say the current texts are too shallow, or that they present too many ideas without enough motivating examples. The chapters often come across as separate, unrelated subjects, and students don't know what technique to apply to a new problem. Outsiders have criticized AI for concentrating on toy domains, and insiders complain that the texts perpetuate this perception by devoting so much space to Eliza, GPS, and other toy programs of the 1960s. Finally, the texts often have gratuitous differences in terminology and notation that make AI appear alien to the well-rounded computer science student.In reaction to these problems, we have written a text 1 that we believe presents the field in a much better light. The text has now been used in over a hundred courses and has been very well received. In this paper we outline the key pedagogical ideas behind it.2 Unified presentation of the field Some texts are organized from a historical perspective, describing each of the major problems and solutions that have been uncovered in 40 years of AI research. Although there is value to this perspective, the result is to give the impression of a dozen or so barely related subfields, each with its own techniques and problems. We have chosen to reinterpret some past research and show how it fits within a common framework and how it relates to other work that was historically separate. Often this inwflves regularizing the notation to emphasize similarities rather than differences. In some cases, we omit work that was important in its day but has since been superseded. 1Artificial Intelligence: A Modern Approach, Prentice Hall. 3 Emphasis on Relations to Previous ExperienceIn all cases, we emphasize the connections that AI has to other areas of computer science, and, to a lesser extent, to mathematics, linguistics, psychology, and philosophy.For example, many of our students have had a compiler course, and know all about BNF, LR(k) grammars, and various parsing algorithms. A course that teaches ATN grammars fails to connect to these students' previous knowledge. We present parsing by first using BNF notation, and then showing what additions are needed to handle semantics, pragmatics and disambiguation. We recognize that ATNs have an important historical place in AI, but we treat them in a Bibliographical and Historical Notes section rather than in the main text. On the o...
