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).

My investigations fall on the experimental psychology side of expert system engineering, specifically the problem of generating methods for extracting the knowledge of experts. I do not review the relevant literature on the cognition of experts.l I want to share a few ideas about research methods that I found worthwhile as I worked with expert interpreters of aerial photographs and other remotely sensed data [Hoffman 1984) and on a project involving expert planners of airlift operations (Hoffman 1986). These ideas should be useful to knowledge engineers and others who might be interested in developing an expert system. In generating expert systems, one must begin by characterizing the knowledge of an expert.

This article discusses several effective techniques for expert system knowledge acquisition based on the techniques that were successfilly used to develop the Central Office Maintenance Printout Analysis and Suggestion System (COMPASS) Knowledge acquisition is not a science, and expert system developers and experts must tailor their methodologies to fit their situation and the people involved. This knowledge is then implemented to form an expert system. The essential part of an expert system is its knowledge, and therefore, knowledge acquisition is probably the most important task in the development of an expert system. In this article, several effective techniques for expert system knowledge acquisition are discussed based on the techniques that were successfully used at GTE Laboratories to develop the COMPASS expert system. Knowledge acquisition for expert system development is still a new field and not (yet?) a science.