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. B. Chandrasekaran has been the director of the group since its inception in the late 1970s.

Although cognitive AI is not generally viewed as being "scientific" in the same, strong sense as is physics, it shares a number of the properties of the natural sciences, especially biology. Certain of special themes of biology, notably the principles of historicity and of structure-function relations, are applicable in AI research. From a biologist's viewpoint, certain principles of cognitive AI research emerge.

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.

Although cognitive AI is not generally viewed as being "scientific" in the same, strong sense as is physics, it shares a number of the properties of the natural sciences, especially biology. Certain of special themes of biology, notably the principles of historicity and of structure-function relations, are applicable in AI research. From a biologist's viewpoint, certain principles of cognitive AI research emerge.

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.

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.

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.

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).

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.

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.