The MITRE Corporation is a scientific and technical an acronym for Knowledge-Based System. Subsequently, organization engaged in system engineering activities, Rome Air Development Center took over support of the principally in support of the United States Air Force and project and continues to fund part of our AI research effort. MITRE is a special kind of engineering MITRE's current research is summarized below. The corporation is a Federal Contract Bedford center is supported by 15 Symbolics Lisp machines Research Center, a designation covering the handful netted to two Vax-780 file servers, while the Washington of independent institutions that perform governmentsponsored center is supported by both a classified and an unclassified research. It is an independent, nonprofit corporation facility, with 2 Lambdas and 2 Symbolics Lisp machines designed and m.anagcd to provide long-term assistance respectively netted to Vax-780 file servers.

The choice of implication as a representation for empirical associations and for deduction as a model of inference requires a mechanism extraneous to deduction to manage uncertainty associated with inference. Consequently, the interpretation of representations of uncertainty is unclear. Representativeness, or degree of fit, is proposed as an interpretation of degree of belief for classification tasks. The calculation of representativeness depends on the nature of the associations between evidence and conclusions. Patterns of associations are characterized as endorsements of conclusions. We discuss an expert system that uses endorsements to control the search for the most representative conclusion, given evidence.

The enterprise of developing knowledge-based systems is currently witnessing great growth in popularity. The central unity of many such programs is that they interpret knowledge that is explicitly encoded as rules. While rule-based programming comes with certain clear pay-offs, further fundamental advances in research are needed to extend the scope of tasks that can be adequately represented in this fashion. This article is a statement of personal perspective by a researcher interested in fundamental issues in the symbolic representation and organization ok knowledge.

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.

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.

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.

About a year ago. I was approached by Phoebe Huang of Comtex Scientific Corporation who hoped that I would help devise a dramatically expanded index for topics in AI to aid Comtex in indexing the series of AI memos and reports that they had been gathering. Comtex had tried to get the ACM to expand and update its classification. But was told that ACM had just revised the listing two years ago or so ago, and did not intend to revise it again for a while: even if they did. The revision might require a year or more to complete. Comtex wanted the new classification within six to eight weeks. I agreed to take on the task, thinking it wouldn't be too hard. The major decision I had to make was whether to use the existing ACM index scheme and add to it, or start with a fresh sheet of paper and devise my own. I decided to stick with ACM's top two levels, only adding, not modifying, major headings.

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

The certainty-factor (CF) model is a commonly used method for managing uncertainty in rule-based systems. We review the history and mechanics of the CF model, and delineate precisely its theoretical and practical limitations. In addition, we examine the belief network, a representation that is similar to the CF model but that is grounded firmly in probability theory. We show that the belief-network representation overcomes many of the limitations of the CF model, and provides a promising approach to the practical construction of expert systems.

Knowing this provides a basis of the art and consideration of the future.