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.

Expert system projects are often based on collaboration with single domain expert. This leads to difficulties in judging the suitability of the chosen task and in acquiring the detailed knowledge required to carry out the task. This anecdotal article considers some of the advantages of using a diverse collection of domain experts.

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.

By-Laws of Association for the Advancement of Artificial Intelligence

In the first section, some characteristic features of AI research in France are presented, including difficulties with the current means and the current organization of AI research. In the second section, the state-of-the-art in different areas of AI is described. Besides some weakness, and in spite of the general difficulties mentioned in the first section, strong points and great potentialities are exhibited. This allows us to conclude that AI research in France may play an important part at the international level, if the necessary means for its development in the middle and long term are given.