Although the group at System Development Corp. (Paoli, Pennsylvania), techniques being studied should be widely applicable, we are with each group responsible for certain aspects of system specifically developing a system to understand paragraphlength design. Our groups are jointly responsible for integration of messages about equipment failures, with the aim of the next-generation text-processing system as part of the Defense summarizing each failure and assessing its impact. Advanced Research Projects Agency (DARPA) Strategic Several laboratory prototypes have been constructed for Computing Program (Grishman and Hirschman 1986). We aim to improve on these earlier a small question-answering system that answers simple systems through a combination of two techniques: the use of English queries about a student transcript database This system detailed domain knowledge to verify and complete our linguistic is used for teaching and as a preliminary test bed for analyses and the use of "forgiving" algorithms that some of our linguistic analysis techniques. Participants: Ralph Grishman (faculty); Tomasz Ksiezyk, To guide the development of our system, we selected a Ngo Thank Nhan, Michael Moore, and John Sterling corpus of messages describing the failure of one particular piece of equipment, a starting air compressor.

This article describes an effort to develop a knowledge-based financial marketing consultant system. Financial marketing is an excellent vehicle for both research and application in artificial intelligence (AI). This domain differs from the great majority of previous expert system domains in that there are no well-defined answers (in traditional sense); the goal here is to obtain satisfactory arguments to support the conclusions made. A large OPS5-based system was implemented as an initial prototype. We present the organization and principles underlying this system and offer our ongoing research directions. The experience gained in the initial prototyping effort is currently being used to further expert systems research and to develop an extensive system that ultimately can be used by the marketing organization.

Initial Development Approach In the following eight subsections, we present a brief discussion of methodology for expert system development, selection of problem and tools, knowledge engineering and prototype implementation, operational feasibility, and the actual development of a working prototype of a process planning expert system. Methodology for Expert System Development Expert systems require a software development methodology that differs in some respects from those methodologies used for conventional systems. Most knowledge-based development methodologies used by organizations experienced in building expert systems are similar in that they concentrate on the early (feasibility) stages of a project. Very little has been published on the later stages, which are concerned with expert system delivery, integration, and maintenance. During the development of OPGEN, we incorporated the lessons learned in these early stages and revised our original approach to provide for integration and maintenance. Most expert system development methodologies are a variation on the following theme, which paraphrases Haycs-Roth (1985): (1) expert system technology is determined to be relevant to a product; (2) management provides an opportunity for action; (3) a preliminary business application is assembled; (4) a knowledge engineering consultant verifies the opportunity; (5) a knowledge engineering project team is formed and assesses the knowledge; (6) the knowledge engineering project manager plans the project; (7) the user organization Figure 2 OPGEN bzput Circuit Layout Diagram.

Large engineering projects, such as the engineering development of computers, involve a large number of activities and require cooperation across a number of departments. Due to technological and market uncertainties, these projects involve the management of a large number of changes. The Callisto project was born out of realization that the classical approaches to project management do not provide sufficient functionally to manage large engineering projects. Callisto was initiated as a research effort to explore project scheduling, control and configuration problems during the engineering prototype development of large computer systems and to devise intelligent project management tools that facilitate the documentation of project management expertise and its reuse from one project to another. In the first phase of the project, rule-based prototypes were used to build quick prototypes of project management expertise and the project management knowledge required to support expert project managers. In the second phase, the understanding of point solutions was used to capture the underlying models of project management in distributed project negotiations and comparative analysis. This article provides an overview of the problems, experiments, and the resulting models of project knowledge and constraint-directed negotiation.

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The development of an online turbine generator diagnostic system is described from conception to initial field verification. The system is composed of a data center located in the power plant that collects data from online measurement devices and communicates these data to a centralized diagnostic facility in Orlando, Florida, where the actual diagnosis is done. The resulting diagnosis and recommended actions are transmitted to the power plant where they are displayed to the operator by the data center. The market-place need, initial approaches to the product, system field verification are described.

This article provides a discussion of factory design and an artificial intelligence (AI) approach to this problem. Major issues covered include knowledge acquisition and representation, design methodology, system architecture, and communication. The facilities design expert systems (FADES developed by the author is presented and described to illustrate issues in factory design.

This article provides a discussion of factory design and an artificial intelligence (AI) approach to this problem. Major issues covered include knowledge acquisition and representation, design methodology, system architecture, and communication. The facilities design expert systems (FADES developed by the author is presented and described to illustrate issues in factory design.

The development of an online turbine generator diagnostic system is described from conception to initial field verification. The system is composed of a data center located in the power plant that collects data from online measurement devices and communicates these data to a centralized diagnostic facility in Orlando, Florida, where the actual diagnosis is done. The resulting diagnosis and recommended actions are transmitted to the power plant where they are displayed to the operator by the data center. The market-place need, initial approaches to the product, system field verification are described. The artificial intelligence (AI) diagnostic program has been diagnosing seven large utility generators since July 1984 and has correctly diagnosed a significant number of generator and instrumentation problems. Issues such as a centralized approach, rule base quality control, and the range of resources needed for a successful product are discussed.

A new computer language for manufacturing is being used to link complex systems of equipment whose components are supplied by multiple vendors. The Cell Management Language (CML) combines computational tools from rule-based data systems, object-oriented languages, and new tools that facilitate language processing. These language tools, combined with rule processing, make it convenient to build new interpreters for interfacing and understanding a range of computer and natural languages ; hence, CML is being used primarily to define other languages in an interpretive environment, that is, as a meta-interpreter. For example, in CML it is quite easy to build an interpreter for machine tool languages that can understand and generate new part programs. Once interpreters for different machine and human languages have been constructed, they can be linked together into a system of interpreters. These interpreters can be used to make intelligent decisions for systemwide action planning and diagnostic error recovery. CML is being used in the factory environment to make turbine blade performs and has proven to greatly simplify the task of building complex control systems.