It is written with great intelligence and insight and can benefit a wide audience from advanced undergraduates to seasoned researchers. It is a book that should be in the permanent collection of every AI aficionado because it is such a rich source of ideas and examples. It is not a full-blown AI text; it does not depend on the reader having any previous knowledge of AI but does assume some basic knowledge of Lisp. I have used this book with great success as a supplement in an introductory graduate AI course, the text in a graduate AI course focusing on techniques, and a resource in my research group. The sheer amount of material is impressive, including symbolic mathematics, logic programming, natural language, expert systems, games, and more.

To be considered exceptional, a textbook must satisfy three basic requirements. First, it must be authoritative, written by one with a broad range of experience in, and knowledge of, a subject. Second, it must effectively communicate to the reader, in the same manner in which a course instructor must be capable of imparting knowledge to students in a classroom. Third, it must stimulate the reader into thinking more deeply about the subject and into viewing it from fresh perspectives. In Artificial Intelligence: A Knowledge-Based Approach (Boyd & Fraser, Boston, 740 pp., $48.95), author Morris W. Firebaugh has succeeded in meeting each of these requirements.

The Sixth Annual Knowledge-Based Software Engineering Conference (KBSE-91) was held at the Sheraton University Inn and Conference Center in Syracuse, New York, from Sunday afternoon, 22 September, through midday Wednesday, 25 September. The KBSE field is concerned with applying knowledge-based AI techniques to the problems of creating, understanding, and maintaining very large software systems. The Sixth Annual Knowledge-Based Software Engineering Conference (KBSE-91) was held at the Sheraton University Inn and Conference Center in Syracuse, New York, from Sunday afternoon, 22 September, through midday Wednesday, 25 September. This conference was sponsored by Rome Laboratory (previously Rome Air Development Center) and was held in cooperation with the Association for Computing Machinery and the American Association for Artificial Intelligence. The origin of KBSE-91 is as follows: In 1983, Rome Air Development Center published a report calling for the development of a knowledgebased software assistant (KBSA) that would use AI techniques to support all phases of the software development process (Green et al. 1986).

It is organized around projects as "a history and assessment of efforts to mechanise processes of translating" (p.18). It is complete, discussing basically every project in the world since machine translation's first glimmerings 40 years ago Projects are grouped by time frame, nation, or approach. The organization is, of course, somewhat arbitrary, but it is supplemented by cross-references and summary tables of projects and systems. Hutchins not only presents the theories, algorithms, and designs but also the history, goals, assumptions, and constraints of each project. There are many sample outputs and fair evaluations of the contributions and shortcomings of each approach.

It is organized around projects as "a history and assessment of efforts to mechanise processes of translating" (p.18). It is complete, discussing basically every project in the world since machine translation's first glimmerings 40 years ago Projects are grouped by time frame, nation, or approach. The organization is, of course, somewhat arbitrary, but it is supplemented by cross-references and summary tables of projects and systems. Hutchins not only presents the theories, algorithms, and designs but also the history, goals, assumptions, and constraints of each project. There are many sample outputs and fair evaluations of the contributions and shortcomings of each approach.

It is organized around projects as "a history and assessment of efforts to mechanise processes of translating" (p.18). It is complete, discussing basically every project in the world since machine translation's first glimmerings 40 years ago Projects are grouped by time frame, nation, or approach. The organization is, of course, somewhat arbitrary, but it is supplemented by cross-references and summary tables of projects and systems. Hutchins not only presents the theories, algorithms, and designs but also the history, goals, assumptions, and constraints of each project. There are many sample outputs and fair evaluations of the contributions and shortcomings of each approach.

Conceptual Spaces--The Geometry of Thought is a book by Peter Gärdenfors, professor of cognitive science at Lund University, Sweden. Gärdenfors has authored another book in this series (based on work with Carlos Alchourron and David Makinson), Knowledge in Flux, a definitive account of the widely examined AGM (after Alchourron, Gärdenfors, and Makinson) theory of belief revision. The AGM theory is firmly based on classical logic and its model theory, and by his founding participation in developing it, Gärdenfors has earned the right to critique knowledge representation. His new book is not primarily about logic, but it is certainly not an apostasy either. If I may be permitted a minor irreverence, I would say that this book came not to destroy logic but to fulfill.

Two topics are treated here First, we present a user model pattcrncd after the stereotype approach (Rich, 1979) This model surpasses Rich's model with respect to its greater flexibility in the construction of user profiles, and its trcat,ment of positive and negative arguments. Second, we present an inference machine This machine treats uncertain knowledge in t,he form of evidence for and against the accuracy of a proposition. Assuming a homogeneous user group, systems developers were able to design a system to perform in accordance with the requirements and capabilities assumed for a partirulal type of user (implicit user modeling). With a heterogeneous user group, this is no longer possible. Since self-assessment,s usually render a distorted picture of the user and are not expected in a real consultative dialogue, they should not be specially required in man-machine communication.

This article discusses t,he selection of the domain for a knowledge-based expert system for a corporate application The selection of the domain is a critical task in an expert system development At the st,art of a project looking into the development of an expert, syst,em, the knowledge engineering project team must investigate one or several possible expert system domains They must decide whether the selected application(s) are best suited to solution by present expert system technology, or if there might he a hettel way (or, possibly, no way) to attack the problems. If there arc several possibilities, the team must also rank the potential applications and select the best availahlc To evaluate the potential of possible application domains, it has proved very useful to have a set of desired at,trihutes for a good expert system domain. This art,iclc presents such a set of attrihut,es The at,trihute set was developed as part of a major expert system development project at GTE Lahorat.ories. In particular, it focuses on selecting an expert system domain for a corporate application. One of the prime arcas of corporate interest is expert systems.

The growth in the amount of available databases far outstrips the growth of corresponding knowledge. This creates both a need and an opportunity for extracting knowledge from databases. Many recent results have been reported on extracting different kinds of knowledge from databases, including diagnostic rules, drug side effects, classes of stars, rules for expert systems, and rules for semantic query optimization. The importance of this topic is now recognized by leading researchers. Michie predicts that "The next area that is going to explode is the use of machine learning tools as a component of large scale data analysis'' (AI Week, March 15, 1990).