It is interesting to note that there is no agreed upon definition of artificial intelligence. However, artificial intelligence has been around for 30 years, so one might wonder why our wheels are still spinning. Below, an attempt is made to answer this question and show why, in a serious sense, artificial intelligence can never demonstrate an outright success within its own discipline. In addition, we will see why the old bromide that "as soon as we understand how to solve a problem, it's no longer artificial intelligence" is necessarily true.

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. To evaluate the potential of possible application domains, it has proved very useful to have a set of desired attributes for good expert domain. 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.

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.

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.