This panel, which met in Austin, Texas, discussed the "deep unease among AI researchers who have been around more than the last four years or so ... that perhaps expectations about AI are too high, and that this will eventually result in disaster."

One reason is that the requirements-oriented methods and intuitions learned in the development of other types of software do not carry over well to the knowledge engineering task. Another reason is that methodologies for developing expert systems by extracting, representing, and manipulating an expert's knowledge have been slow in coming. At Tektronix, we have been using step-by-step approach to prototyping expert systems for over two years now. This methodology has helped us collect the knowledge necessary to implement several prototype knowledge-based systems, including a troubleshooting assistant for the Tektronix FG-502 function generator and an operator's assistant for a wave solder machine.

General Electric is engaged in a broad range of research and development activities in artificial intelligence, with the dual objectives of improving the productivity of its internal operations and of enhancing future products and services in its aerospace, industrial, aircraft engine, commercial, and service sectors. Many of the applications projected for AI within GE will require significant advances in the state of the art in advanced inference, formal logic, and architectures for real-time systems. New software tools for creating expert systems are needed to expedite the construction of knowledge bases. Further, new application domains such as computer -aided design (CAD), computer- aided manufacturing (CAM), and image understanding based on formal logic require novel concepts in knowledge representation and inference beyond the capabilities of current production rule systems. Fundamental research in artificial intelligence is concentrated at Corporate Research and Development (CR&D), with advanced development and applications pursued in parallel efforts by operating departments. The fundamental research and advanced applications activities are strongly coupled, providing research teams with opportunities for field evaluations of new concepts and systems. This article summarizes current research projects at CR&D and gives an overview of applications within the company.

The fact was there were a lot of failures. There I have been assigned the role of survivalist. First I want to were overruns and systems delivered past schedule. This ask, "Has AI paid its way?"... Or to put it another way, is certainly not unique to Naval Electronic System Command. "Have we earned our keep?" I have three answers to that: The most would be systems being acquired for the Yes, yes, and yes.

And even if verification to be accommodated within the SPIV paradigm. But until were possible it would not contribute very much to the such time as we find these learning algorithms (and I development of production software. Hence "verifiability don't think that many would argue that such algorithms must not be allowed to overshadow reliability. Scientists will be available in the foreseeable future) we must face should not confuse mathematical models with reality." the prospect of systems that will need to be modified, in AI is perhaps not so special, it is rather an extreme nontrivial ways, throughout their useful lives. Thus incremental and thus certain of its characteristics are more obvious development will be a constant feature of such than in conventional software applications. Thus the SPIV software and if it is not fully automatic then it will be part methodology may be inappropriate for an even larger class of the human maintenance of the system. I am, of course, of problems than those of AI. not suggesting that the products of say architectural design I have raised all these points not to try to deny the (i.e., buildings) will need a learning capability. Nevertheless, worth of Mostow's ideas and issues concerning the design a final fixed design, that remains "optimal" in a process, but to make the case that such endeavors should dynamically changing world, is a rare event.The similarity also be pursued within a fundamentally incremental and between AI system development and the design of more evolutionary framework for design. The potential of the concrete objects is still present, but it is, in some respects, RUDE paradigm is deserving of more attention than it is rather tenuous I admit.

The founding of a new college at Rutgers in 1969 became the occasion for building a strong computer science presence in the University. Livingston College thus provided the home for the newly organized Department of Computer Science (DCS) and for the beginning of computer science research at Rutgers.

Research in AI within the Computer Science Department at the University of California, Los Angeles is loosely composed of three interacting and cooperating groups: (1) the Artificial Intelligence Laboratory, at 3677 Boelter Hall, which is concerned mainly with natural language processing and cognitive modelling, (2) the Cognitive Systems Laboratory, at 4731 Boelter Hall, which studies the nature of search, logic programming, heuristics, and formal methods, and (3) the Robotics and Vision Laboratory, at 3532 Boelter Hall, where research concentrates on robot control in manufacturing, pattern recognition, and expert systems for real-time processing.

The fifth Distributed Artificial Intelligence Workshop was held at the Schlumberger-Doll Research Laboratory from October 14 to 17, 1984. It was attended by 20 participants from academic and industrial institutions. As in the past, this workshop was designed as an informal meeting. It included brief research reports from individual groups along with general discussion of questions of common interest. This report summarizes the general discussion and contains summaries of group presentations that have been contributed by individual speakers.

A variety of proposals for preferred methodological approaches has been advanced in the recent artificial intelligence (AI) literature. Rather than advocating a particular approach, this article attempts to explain the apparent confusion of efforts in the field in terms of differences among underlying methodological perspectives held by practicing researchers. The article presents a review of such perspectives discussed in the existing literature and then considers a descriptive and relatively specific typology of these differing research perspectives. It is argued that researchers should make their methodological orientations explicit when communicating research results, to increase both the quality of research reports and their comprehensibility for other participants in the field. For a reader of the AI literature, an understanding of the various methodological perspectives will be of immediate benefit, giving a framework for understanding and evaluating research reports. In addition, explicit attention to methodological commitments might be a step towards providing a coherent intellectual structure that can be more easily assimilated by newcomers to the field.

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.