The fifth Distributed Artificial Intelligence Workshop was held at the Schlumberger-Doll Research Laboratory from October 14 to 17, 1984. It was attended by 20 participants from academic and industrial institutions. As in the past, this workshop was designed as an informal meeting. It included brief research reports from individual groups along with general discussion of questions of common interest. This report summarizes the general discussion and contains summaries of group presentations that have been contributed by individual speakers.

I want to discuss two aspects of technology transfer. It took a committee several years to come up automation. Then I want to give my two cents worth on with this. It has a lot of words and you can't understand AI as a business activity. Interestingly enough, so does My particular focus is on commercial AI, that is, products Europe. The United States is dead last in utilizing this that incorporate AI that are being sold for profit, as technology-most of which came out of U.S. industry and opposed to "practical" AI, in which AI is incorporated into AI labs. The definition we used at Machine Intelligence views Commercial AI products take the form of equipment, a robot as a computer system with a peripheral attached systems, or software. This forces a different view of a robot, a of demonstrated successes in artificial intelligence systemsmaybe view that is useful in actually thinking about applications.

A variety of proposals for preferred methodological approaches has been advanced in the recent artificial intelligence (AI) literature. Rather than advocating a particular approach, this article attempts to explain the apparent confusion of efforts in the field in terms of differences among underlying methodological perspectives held by practicing researchers. The article presents a review of such perspectives discussed in the existing literature and then considers a descriptive and relatively specific typology of these differing research perspectives. It is argued that researchers should make their methodological orientations explicit when communicating research results, to increase both the quality of research reports and their comprehensibility for other participants in the field. For a reader of the AI literature, an understanding of the various methodological perspectives will be of immediate benefit, giving a framework for understanding and evaluating research reports. In addition, explicit attention to methodological commitments might be a step towards providing a coherent intellectual structure that can be more easily assimilated by newcomers to the field.

The choice of implication as a representation for empirical associations and for deduction as a model of inference requires a mechanism extraneous to deduction to manage uncertainty associated with inference. Consequently, the interpretation of representations of uncertainty is unclear. Representativeness, or degree of fit, is proposed as an interpretation of degree of belief for classification tasks. The calculation of representativeness depends on the nature of the associations between evidence and conclusions. Patterns of associations are characterized as endorsements of conclusions. We discuss an expert system that uses endorsements to control the search for the most representative conclusion, given evidence.

Two topics are treated here. First we present a user model patterned after the stereotype approach (Rich, 1979). This model surpasses Rich's model with respect to it's greater flexibility in the construction of user profiles, and it's treatment of positive and negative arguments. Second, we present an inference machine. This machine treats uncertain knowledge in the form of evidence for and against the accuracy of a proposition. Truth values are replaced by the concept of two-dimensional evidence space. We discuss the consequences of the concept, particularly with regard to verification. The connection between these two topics is established by implementation of the user model on the inference machine.

This article presents a summary of ongoing, funded artificial intelligence research at North Carolina State University. The primary focus of the research is engineering aspects of artificial intelligence. These research efforts can be categorized into four main areas: engineering expert systems, generative database management systems, human-machine communication, and robotics and vision. Involved in the research are investigators from both the School of Engineering and the Department of Computer Science. The research programs are currently being sponsored by the Center for Communications and Signal Processing (CCSP), the Integrated Manufacturing Systems Engineering Institute (IMSEI), the National Aeronautics and Space Administration (NASA), the National Science Foundation (NSF) and the United States Department of Agriculture (USDA).

This year, the AAAI has alrrady wanted or needed such information. Int,ernational and Par Technology; can continue to ensure delivery of Coupling Symbolic and Numeracal Thank you for your cooperation. Richard Fikes reported that the Menlo Park, CA 94025-3496. Carnegie-Mellon Univcrsit,y, Membership Statistics: the final, complete results of the survey AAAI Office During the first quarter of 1985, the will he published in a forthcoming Claudia Mazzet,ti reported that the membership roster expanded from issue of the AI Mugazane. Association's databases and a set 7,492 to 8,651 members.

The AI Group at The Ohio State University conducts a broad range of research projects in knowledge-based reasoning. The primary focus of this work is on analyzing problem solving, especially within knowledge -rich domains. In information processing or knowledge-level terms. B. Chandrasekaran has been the director of the group since its inception in the late 1970s.

Letters to the editor on genetics and applied epistemology, a response to "The Professor's Challenge" and a response to John Malpas; knowledge and power, and update on the Autoling System, a relativistic approach, and a response to Franklin's writings in volume 6 number 1.

This article discusses the 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 start of a project looking into the development of an expert system, 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 be a better way (or, possibly, no way) to attack the problems. If there are several possibilities, the team must also rank the potential applications and select the best available. To evaluate the potential of possible application domains, it has proved very useful to have a set of desired attributes for good expert domain. This article presents such a set of attributes. The attribute set was developed as part of a major expert system development project at GTE Laboratories. It was used recurrently (and modified and expanded continually) throughout an extensive application domain evaluation and selection process.