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).

An important task in postal automation technology is determining the position and orientation of the destination address block in the image of a mail piece such as a letter, magazine, or parcel. The corresponding subimage is then presented to a human operator or a machine reader (optical character reader) that can read the zip code and, if necessary, other address information and direct the mail piece to the appropriate sorting bin. Analysis of physical characteristics of mail pieces indicates that in order to automate the address finding task, several different image analysis operations are necessary. Some examples are locating a rectangular white address label on a multicolor background, progressively grouping characters into text lines and text lines into text blocks, eliminating candidate regions by specialized detectors (for example, detecting regions such as postage stamps), and identifying handwritten regions. Described here are several operations, their utility as predicted by statistics of mail piece characteristics, and the results of applying the operations to a task set of mail piece images. A problem-solving architecture based on the blackboard model of problem solving for appropriately invoking the tools and combining their results is described.

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.

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

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.

Furniture, fixtures and equipment are stated word processing program.

This report contains a historical perspective on previous Distributed Artificial Intelligence Workshops, highlights of the roundtable discussions, and a collection of research abstracts submitted by the participants.

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.

The (CSCW '86) was held in Austin, participants). The three-day report introduces the field of computersupported Texas, on 3-5 December 1986. It was event included nine paper sessions: cooperative work, describes the sponsored by the Microelectronics supporting face-to-face groups, empirical CSCW '86 program, and discusses the significance and Computer Technology Corporation studies, supporting distributed of the conference results An (MCC) Software Technology Program groups, hypertext systems, underlying introduction to the follow-on conference, in cooperation with the Association technology for collaborative systems, CSCW '88, is also provided for Computing Machinery (ACM) collaboration research, multimedia and its special interest groups on software and multiuser interfaces, industrial engineering (SIGSOFT), human experiences with CSCW, and coordination computer interaction (SIGCHI), and and decision making. There office information systems (SIGOIS); were also four panel sessions; the topics the Institute for Electrical and Electronic were collaboration and offices, collaborative Engineers (IEEE) Computer design studies, from theories Society; the American Association for to systems, and trends and markets Artificial Intelligence (AAAI); The for computer-supported group Information Management Society work. As the invited dinner speaker, (TIMS); and the Software Psychology Robert Howard, noted author on the Society.

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.