On October 17-19, 1984, a workshop on nonmonotonic hospitality suite-generally until late in the evenings reasoning was held at, Mohonk Mountain House, outside The workshop's only disappointment was the shortness New Paltz, New York. Speakers (and the audience) oft,en found Raymond R.eit,er and Bonnie Webbcr, and was sponsored that much more time could have been well-spent, especially by the American Association for Artificial Intelligence. The hotel is an inmense of much of the work presented. Surrounded by 2000 Preprints of the papers were distributed at the workshop, acres of private preserve, in full autumnal splcndour, participants but no proceedings will be published A limit,ed number quickly forgot the outside world. The grounds of copies of the preprints can be obtained from.

The Artificial Intelligence in Medicine (AIM) Workshop has become a tradition. Meeting every year for the past nine years, it has been the forum where all the issues from basic research through applications to implementations have been discussed; it has also become a community building activity, bringing together researchers, medical practitioners, and government and industry sponsors of AIM activities. The AIM Workshop held at Fawcett Center for Tomorrow at Ohio State University, June 30 - July 3, 1984, was no exception. It brought together more than 100 active participants in AIM.

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.

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.

The University of Michigan is the site of a variety of AI research projects involving faculty, staff and students from several departments and institutes on the Ann Arbor campus.

The TRW Defense Systems Group develops large man-machine networks that solve problems for government agencies. Until a few years ago these networks were either tightly-coupled humans loosely supported by machines -- like our ballistic missile system engineering organization, which provides technical advice to the Air Force, or tightly-coupled machines loosely controlled by humans- like the ground station for the NASA Tracking and Data Relay Satellite System. Because we have been producing first-of- a kind systems like these since the early 1950s, we consider ourselves leaders in the social art of assembling effective teams of diverse experts, and in the engineering art of conceiving and developing networks of interacting machines. But in the mid-1970s we began building systems in which humans and machines must be tightly coupled to each other-systems like the Sensor Data Fusion Center. Then we found that our well-worked system development techniques did not completely apply, and that our system engineering handbook needed a new chapter on communication between people and machines. We're still writing that chapter, and it won't be finished until we can add some not-yet fully developed artificial intelligence techniques. Nevertheless, we learned some lessons worth passing along.

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.