The Center for Automation and Intelligent Systems monocrystal turbine blades for jet engines that are made Research at Case Western Reserve University, founded by investment casting. Essentially, the part is made by in 1984, provides the setting and the administrative and pouring liquid metal into a ceramic mold, but the environment funding mechanisms for coordinating and focusing the capabilities in which this is done must be tightly controlled. of faculty members and students from many There are several other subprocesses that are also tightly disciplines and departments to deal with significant realworld controlled, such as making the mold. The total process is too complex for a single expert The center serves as an interface between separate system; rather, several different expert systems are needed basic research efforts in the various disciplines and academic and should be coordinated in some way, perhaps by a more departments and the multidisciplinary group efforts global expert system. Currently, we are constructing an needed to deal effectively with nontrivial real problems. Wax patterns appear to be essential for the factory of the future.

This report contains the following discussions: the defense program simulation of rocky flats plant; spatial representation and reasoning for automated mesh generation; INEL support to modernization efforts at the aberdeen proving ground; artificial intelligence applications at the ICPP; an expert system for tuning particle beam accelerators; quality control expert system; an easily maintained knowledge-based system for interactive delivery of detailed technical information; workload scheduling in DOE production complex; turning operations planning system; a nuclear power plant operator advisor based on artificial intelligence technology; a impact of artificial intelligence on the new production reactor; using expert systems in treaty verification; knowledge-basedmore » systems technology transfer in Oak Ridge; applications of AI to nuclear power plants; knowledge-based computer security systems; robotic grasping of unknown objects: a knowledge-based approach; applying expertise to data in the geologist's assistant expert system; feature recognition based automatic part classification and coding; object-oriented inventories for simulation of manufacturing process; expert system at AWE; plating expert system; inspection process planning expert; troubleshooting local area networks at Savannah River Site; maintenance importance generator; joint theater level simulator; a system for authoring of tutorials including video capture and annotation, links to manuals, and links to executable code; a personal computer based expert system for documenting compliance with the National Environmental Protection Act; spatial representation and reasoning for automated mesh generation; robotic grasping of unknown objects: a knowledge-based approach; and synthesis of engineering anticipatory systems.«

The major limitations in building large software have always been (a) its brittleness when confronted by problems that were not foreseen by its builders, and (by the amount of manpower required. The recent history of expert systems, for example highlights how constricting the brittleness and knowledge acquisition bottlenecks are. Moreover, standard software methodology (e.g., working from a detailed "spec") has proven of little use in AI, a field which by definition tackles ill- structured problems. How can these bottlenecks be widened? Attractive, elegant answers have included machine learning, automatic programming, and natural language understanding. But decades of work on such systems have convinced us that each of these approaches has difficulty "scaling up" for want a substantial base of real world knowledge.

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. 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.

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.

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.

In the first section, some characteristic features of AI research in France are presented, including difficulties with the current means and the current organization of AI research. In the second section, the state-of-the-art in different areas of AI is described. Besides some weakness, and in spite of the general difficulties mentioned in the first section, strong points and great potentialities are exhibited. This allows us to conclude that AI research in France may play an important part at the international level, if the necessary means for its development in the middle and long term are given.

Gives a complete account of the mathematical structure, statistical analysis, and applications of finite mixture distributions. Direct applications include economics, medicine, remote sensing, sedimentology, and signal detection (pattern recognition). Also describes indirect applications--in outlier models, density estimation, Bayesian and empirical Bayes analysis, and robustness studies. Goes on to cover mathematical concepts such as identifiability and information, and the inferential problems associated with data from a mixture. Approximate sequential methods are developed here, in order to deal with estimation difficulties and engineering applications.

Schlumberger is a large, multinational corporation concerned primarily with the measurement, collection, and interpretation of data. For the past fifty years, most of the activities have been related to hydrocarbon exploration. The efficient location and production of hydrocarbons from an underground formation requires a great deal of knowledge about the formation, ranging in scale from the size and shape of the rock's pore spaces to the size and shape of the entire reservoir. Schlumberger provides its clients with two types of information: measurements, called logs, of the petrophysical properties of the rock around the borehole, such as its electrical, acoustical, and radioactive characteristics; and in terpretations of these logs in terms of geophysical properties such as porosity and mineral composition.

Schlumberger is a large, multinational corporation concerned primarily with the measurement, collection, and interpretation of data. For the past fifty years, most of the activities have been related to hydrocarbon exploration. The efficient location and production of hydrocarbons from an underground formation requires a great deal of knowledge about the formation, ranging in scale from the size and shape of the rock's pore spaces to the size and shape of the entire reservoir. Schlumberger provides its clients with two types of information : measurements, called logs, of the petrophysical properties of the rock around the borehole, such as its electrical, acoustical, and radioactive characteristics; and in terpretations of these logs in terms of geophysical properties such as porosity and mineral composition. Since log interpretation is expert skill, the emergence of expert systems technology prompted Schlumberger's initial interest in Artificial Intelligence. Our first full- scale attempt at a commercial-quality expert system was the Dipmeter Advisor. Following these initial efforts, Schlumberger has expanded its Artificial Intelligence activities, and is now engaged in both basic and applied research in a wide variety of areas.