When I took on the job of programme chairman of IJCAI-83 the trustees presented me with a list of problems with the way IJCAI programmes had traditionally been organized. Some of these problems had been raised by previous programme chairmen, some by attendees and some been subsequently been raised by me. I have tried to organise the IJCAI-83 programme to solve these problems -or at least some of them, I have been unable to devise a scheme which simultaneously solves all the problems. (I leave this as an exercise for the reader.) My plans converged after consultation with many people in the AI community, including the IJCAI-83 conference committee, and they have that committee's approval. Inevitably this means that IJCAI-83 will be a little different from here-to -fore, and in order for my changes to be also solutions, it is necessary for you, the paying customer, to be aware of these differences and to take advantage of them. The aim of this article is to raise you awareness.

The Fairchild Laboratory for Artificial Intelligence Research (FLAIR) was inaugurated in October, 1980, with the purposes of introduction AI Technology into Fairchild Camera and Instrument Corporation, and of broadening the AI base of its parent company, Schlumberger Ltd. The charter of the laboratory includes basic and applied research in all AI disciplines. Currently, we have significant efforts underway in several areas of computational perception, knowledge representation and reasoning, and AI-related architectures. We also engage in various tool-building activities to support our research program. The current computational environment includes several large mainframes dedicated to AI research, a number of high-performance personal scientific machines, and extensive graphics capabilities.

Our group's work in medical decision making has led us to formulate a framework for expert system design, in particular about how the domain knowledge may be decomposed into substructures. We propose that there exist different problem-solving types, i.e., uses of knowledge, and corresponding to each is a separate substructure specializing in that type of problem-solving. Each substructure is in turn further decomposed into a hierarchy of specialist which differ from each other not in the type of problem-solving, but in the conceptual content of their knowledge; e.g.; one of them may specialize in "heart disease," while another may do so in "liver," though both of them are doing the same type of problem solving. Thus ultimately all the knowledge in the system is distributed among problem-solvers which know how to use that knowledge. This is in contrast to the currently dominant expert system paradigm which proposes a common knowledge base accessed by knowledge-free problem-solvers of various kinds. In our framework there is no distinction between knowledge bases and problem-solvers: each knowledge source is a problem-solver. We have so far had occasion to deal with three generic problem-solving types in expert clinical reasoning: diagnosis (classification), data retrieval and organization, and reasoning about consequences of actions. In novice, these expert structures are often incomplete, and other knowledge structures and learning processes are needed to construct and complete them.

THE COGNITION AND PROGRAMMING PROJECT (CAPP) to use such constructs effectively. Dr. Elliot Soloway, Assistant Professor; Dr. Kate which people bring to programming and that computing Ehrlich, Research Associate Lewis Johnson; Jeff Bonar; Valerie Abbott which arise due to cognztively poor programming language constructs. Work is currently in progress on the following projects: What do experts/novices know about programming. 'This project is currently being funded by NSF RISE, under grant'This project is currently being funded by NSF IST, under grant number TIIE AI MAGAZINE Winter/Spring 1083 17 then many individuals will not be able to acquire such languages; Soloway, E., Woolf, B., Rubin, E., Bonar, J. (1982) Overview moreover, it appears beneficial from a problem solving Vancouver, B.C. the empirical projects, we are actively engaged in building an Bonar, J., Ehrlich, K., Soloway, E., Rubin, E. (1982) Collecting AIbased tutoring system, PROUST, which can assist novice Behavioral Research Methods and Instrumentation, this system is to identify non-syntactic bugs in a student's Recent CAPP publications are listed below. What Do Novices Know About Programming?

This paper explores the paradigm that heuristics are discovered by consulting simplified models of the problem domain. After describing the features of typical heuristics on some popular problems, we demonstrate that these heuristics can be obtained by the process of deleting constraints from the original problem and solving the relaxed problem which ensues. We then outline a scheme for generating such heuristics mechanically, which involves systematic refinement and deletion of constraints from the original problem specification until a semidecomposable model is identified. The solution to the latter constitutes a heuristic for the former.

This paper argues for the position that experimental human studies are relevant to most facets of AI research and that closer ties between AI and experimental psychology will enhance the development of booth the principles of artificial intelligence and their implementation in computers. Raising psychological assumptions from the level of ad hoc intuitions to the level of systematic empirical observation, in the long run, will improve the quality of AI research and help to integrate it with related studies in other disciplines.

To Date Gross margin, by activity: Rosenschein will complete McCarthy's term on the Council for

In response to a world in which cancer is a growing global health challenge, there is now a greater need for US Medical Physicists and other Radiation Oncology professionals across institutions to work together and be more globally engaged in the fight against cancer. There are currently many opportunities for Medical Physicists to contribute to alleviating this pressing need, especially in helping enhance access to Medical Physics Education/training and Research Excellence across international boundaries, particularly for low and middle-income countries (LMIC), which suffer from a drastic shortage of accessible knowledge and quality training programs in radiotherapy. Many Medical Physicists aremore » not aware of the range of opportunities that even with small effort could have a high impact. Faculty at the two CAMPEP-accredited Medical Physics Programs in New England: the University of Massachusetts Lowell and Harvard Medical School have developed a growing alliance to increase Access to Medical Physics Education/training and Research Excellence (AMPERE), and facilitate greater active involvement of U.S. Medical Physicists in helping the global fight against cancer and cancer disparities. In this symposium, AMPERE Alliance members and partners from Europe and Africa will present and discuss the growing global cancer challenge, the dearth of knowledge, research, and other barriers to providing life-saving radiotherapy in LMIC, mechanisms for meeting these challenges, the different opportunities for participation by Medical Physicists, including students and residents, and how participation can be facilitated to increase AMPERE for global health.

See also:Uniqueness of the Gaussian Kernel for Scale-Space FilteringUnited States PatentEuropean PatentProceedings of the Eighth International Joint Conference on Artificial Intelligence, Karlsruhe, West Germany, 1019-1022

Swartout accrue from such an endeavor?