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A Knowledge-Based Consultant for Financial Marketing
Kastner, John, Apte, Chidanand, Griesmer, James
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.
Artificial Intelligence Research in Progress at the Courant Institute, New York University
Davis, Ernest, Grishman, Ralph
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.
Review of Artificial Intelligence for Microcomputers: The Guide for Business Decision Makers
Other interesting topics in superb, it still contains inadequacies. This statement is more this chapter include nonmonotonic reasoning and modal and a testament to how remarkably difficult it is to write an adequate intentional logic. Perhaps the most intriguing chapter is introductory AI text than it is a criticism of the job done "Memory Organization and Deduction," which touches by the authors. Because there really are no single volumes upon the topics of frame-based representation and deductive yet that provide a satisfactory introduction to AI, the best retrieval and introduces the time-order representation approaches way to approach the problem of selecting text material for an of temporal system analysis and time map management introductory AI course seems to be use a book such as this .
Editorial
Engelmore, Robert S., Fox, Mark S.
EDITORIALS This Fall issue marks the first time we have devoted the AI Figure 1 summarizes the results of a survey I ran in 1985. The idea originated a couple of depicts the number of AI based systems in the various stages years ago, and I'm pleased to see the actual implementation. of research, development, field service and production use. Mark Fox, Special Editor for this issue, is to be congratulated It is my guess that the survey represents about 15% of the for a fine job of selecting some of the best authorities in systems currently under development. The incursion of AI the field and working with them to produce an excellent survey into the manufacturing world has reached the point that discussions of the current state of the art in AI for manufacturing. The quality of all the articles was so high that we didn't want to exclude any of them.
PIES: An Engineer's Do-It-Yourself Knowledge System for Interpretation of Parametric Test Data
Pan, Jeff Yung-Choa, Tenenbaum, Jay M.
The Parametric Interpretation Expert System (PIES) is a knowledge system for interpreting the parametric test data collected at the end of complex semiconductor fabrication processes. The system transforms hundreds of measurements into a concise statement of all the overall health of the process and the nature and probable cause of any anomalies. A key feature of PIES is the structure of the knowledge base, which reflects the way fabrication engineers reason causally about semiconductor failures. This structure permits fabrication engineers to do their own knowledge engineering, to build the knowledge base, and then to maintain it to reflect process modifications and operating experience. The approach appears applicable to other process control and diagnosis tasks.
CML: A Meta-Interpreter for Manufacturing
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.
Constructing and Maintaining Detailed Production Plans: Investigations into the Development of K-B Factory Scheduling
Smith, Stephen F., Fox, Mark S., Ow, Peng Si
To be useful in practice, a factory production schedule must reflect the influence of a large and conflicting set of requirements, objectives and preferences. Human schedulers are typically overburdened by the complexity of this task, and conventional computer-based scheduling systems consider only a small fraction of the relevent knowledge. This article describes research aimed at providing a framework in which all relevant scheduling knowledge can be given consideration during schedule generation and revision. Factory scheduling is cast as a complex constraint-directed activity, driven by a rich symbolic model of the factory environment in which various influencing factors are formalized as constraints. A variety of constraint-directed inference techniques are defined with respect to this model to provide a basis for intelligently compromising among conflicting concerns. Two knowledge-based factory scheduling systems that implement aspects of this approach are described.
Letters to the Editor
Leighninger, Marcia, Gibbons, Hugh, Friedland, Peter, Ensanian, Minas, Firschein, Oscar
Our development efforts involve small multidisciplinary KLA Instruments Corporation is looking for bright, dedicated teams with backgrounds in CS, EE, Math, Mechanical professionals to meethe challenge of developing Engineering and Physics, oriented to building leading edge knowledge-based systems for machine successful commercial products. You will have the control. We're using existing Al technology to actually opportunity to work on entire life cycle development get new KLA products out the door. If you have experience from idea to first shippable products.
An AI-Based Methodology for 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.
Online, Artificial Intelligence-Based Turbine Generator Diagnostics
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.