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.

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 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. The AIM Workshop held at Fawcett Center for Tomorrow at Ohio State University, June 30 - July 3, 1984, was no exception. It brought together more than 100 active participants in AIM.

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.

The Air Force Institute of Technology (AFIT) provides master's degree education to Air Force and Army Officers in various engineering fields It is in a unique position to educate and perform research in the area of applications of artificial intelligence to military problems. Its two AI faculty members are the only military officers with PhD's in Artificial Intelligence. In the past two years, the artificial intelligence Laboratory of AFIT has become a major focal point for AI research and applications within the government. In this article, we describe our on-going applications research in the areas of automated cockpit systems, natural language understanding, maintenance expert systems, expert systems for planning and knowledge based software design.

NON-VON is a massively parallel machine constructed using custom VLSI chips, each containing a number of simple processing elements A preliminary prototype is now operational at Columbia University The machine is intended to provide highly efficient support for a wide range of artificial intelligence and other symbolic applications This paper briefly describes the current version of the NON-VON machine and presents evidence for its applicability to the execution of OPS5 production systems, a number of low-and intermediate-level computer vision tasks, and certain "difficult" relational algebraic operations relevant to knowledge base management Analytic and simulation results are presented for a number of algorithms The data suggest that NON-VON could provide a performance improvement of as much as two to three orders of magnitude over a conventional sequential machine for a wide range of AI tasks

This paper reports several properties of heuristic best-first search strategies whose scoring functions ƒ depend on all the information available from each candidate path, not merely on the current cost g and the estimated completion cost h. It is shown that several known properties of A* retain their form (with the minmax of f playing the role of the optimal cost), which helps establish general tests of admissibility and general conditions for node expansion for these strategies. On the basis of this framework the computational optimality of A*, in the sense of never expanding a node that can be skipped by some other algorithm having access to the same heuristic information that A* uses, is examined. A hierarchy of four optimality types is defined and three classes of algorithms and four domains of problem instances are considered. Computational performances relative to these algorithms and domains are appraised.

Search algorithms for finding optimal solutions are, at least from the practical point of view, often enough intractible, so that the search for good ('satisficing') solutions becomes a research topic of its own interest. Satisficing solutions and different approaches to obtain them under various criteria is the subject of these notes, published in the series "Research notes in artificial intelligence". In an introductory chapter the author presents the known point - value and the point - { { set of values} } identification used in search- based decision-algorithms for guiding the search and discusses some of their advantages and disadvantages. This motivates the here studied alternative approach using that evaluation functions do not return a point - value or a range of values corresponding to a point (state) in a tree but now a distribution function, that describes the possible location of the'value' of the state. Chapter 2 introduces this model, Chapter 6 resumes the basic results.

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