Two topics are treated here. Second, we present an inference machine. This machine treats uncertain knowledge in the form of evidence for and against the accuracy of a proposition. The connection between these two topics is established by implementation of the user model on the inference machine.

The TRW Defense Systems Group develops large man-machine networks that solve problems for government agencies. 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. 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.

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. 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).

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.

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.

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).

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.

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.