A number of tools that support the development, execution, and maintenance of knowledge-based systems are marketed commercially. Many of these tools, however, are designed for applications that can be executed on personal computers and are not suitable for building large knowledge-based systems. The market for knowledge engineering tools designed for applications that require the computational power of a Lisp machine or an engineering workstation is dominated by a few vendors. This article is an assessment of the current state of tools used to build large knowledge-based systems. This assessment is based on the collective strengths and weaknesses of several tools that have been evaluated. In addition, an estimate is made of the features that will be required in the next generation of tools.

We offer a simple mechanism, that of metalingual definition (D. G. Hays, The Meaning of a Term is a Function of the Theory in Which It Occurs, SIGLASH Newsletter, 1973, 6, 4:S-11).

This article examines the direction in which knowledge bases are constructed for diagnosis and decision making. When building an expert system, it is traditional to elicit knowledge from an expert in the direction in which the knowledge is to be applied, namely, from observable evidence toward unobservable hypotheses. Therefore, we argue that a knowledge base be constructed following the expert's natural reasoning direction, and then reverse the direction for use. This choice of representation direction facilitates knowledge acquisition in deterministic domains and is essential when a problem involves uncertainty.

This article presents an approach to a graduate-level course in expert, knowledge-based, problem-solving systems. The core of the course, and this article, is a set of questions called a profile, that can be used to characterize and compare each system studied.

The questions of how humans process uncertain information is important to the development of knowledge-based systems in term of both knowledge acquisition and knowledge representation. This article reviews three bodies of psychological research that address this question: human perception, human probabilistic and statistical judgement, and human choice behavior. The general conclusion is that human behavior under certainty is often suboptimal and sometimes even fallacious. The requirements for a system designed to reduce the effects of human factors in the processing of uncertain knowledge are introduced.

MA 02115 A Public Service of This Publicaiion 0 1987 National Commission for Cooperative Education page must specify exactly one topic ence proceedings. At most one addi-Please send program suggestions from the above list of topics (as well tional page can be used, at a cost to and inquiries to: as a subtopic, if applicable) as the the authors of $250 Papers exceeding Reid G. Smith main topic of the paper. This information six pages, and papers violating the Schlumberger Palo Alto Research helps determine which members instructions to authors, will not be 3340 Hillview Ave. of the program committee review included in the proceedings.

This article presents an approach to a graduate-level course in expert, knowledge-based, problem-solving systems. The core of the course, and this article, is a set of questions called a profile, that can be used to characterize and compare each system studied.

Krasner is the author of Department of Neurology at the University the "CSCW '86 Summary Report." of California at Davis.

This article examines the direction in which knowledge bases are constructed for diagnosis and decision making. When building an expert system, it is traditional to elicit knowledge from an expert in the direction in which the knowledge is to be applied, namely, from observable evidence toward unobservable hypotheses. However, experts usually find it simpler to reason in the opposite direction-from hypotheses to unobservable evidence-because this direction reflects causal relationships. Therefore, we argue that a knowledge base be constructed following the expert's natural reasoning direction, and then reverse the direction for use. This choice of representation direction facilitates knowledge acquisition in deterministic domains and is essential when a problem involves uncertainty. We illustrate this concept with influence diagrams, a methodology for graphically representing a joint probability distribution. Influence diagrams provide a practical means by which an expert can characterize the qualitative and quantitative relationships among evidence and hypotheses in the apporiate direction. Once constructed, the relationships can easily be reserved into the less intuitive direction in order to perform inference inference and diagnosis. In this way, knowledge acquisition is made cognitively simple; the machine carries the burden of translating the representation.

The questions of how humans process uncertain information is important to the development of knowledge-based systems in term of both knowledge acquisition and knowledge representation. This article reviews three bodies of psychological research that address this question: human perception, human probabilistic and statistical judgement, and human choice behavior. The general conclusion is that human behavior under certainty is often suboptimal and sometimes even fallacious. Suggestions for knowledge engineers in detecting and obviating such errors are discussed. The requirements for a system designed to reduce the effects of human factors in the processing of uncertain knowledge are introduced.