The rapid advances in information technology are causing a fundamental change in the way we do our business. Within our manufacturing business today, various parts of the organization are " reasoning " about "engineered products." The everyday problem-solving activity within the organization can be thought of as conducted by a network of experts knowledgeable about the products and the physical and paperwork processes that constitute the business, that is, the knowledge network. The focus of our attention has not been just at the factory level; we have been addressing the order-process cycle: marketing, sales, order administration, manufacturing, distribution, and field service. This cycle can be thought of as outer loop of the knowledge network. Also, we recently began addressing the inner loop. This loop is the product life cycle : marketing and new product requirements, design and manufacturing startup, and volume or steady-state manufacturing. This article describes DEC's internal strategy for applying artificial intelligence (AI) to manufacturing processes and problems above the work-cell level. In addition to an overview of this knowledge network, we feature DEC's newest system in order processing : the configuration-dependent sourcing (CDS) expert. Project experience on this system, which deals with the assignment of fulfillment sites (factories) to line items in computer system orders, is also described.

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 .

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.

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.

To know that the other suffers, we have to close the gap between us in an act of empathy. Only then can we begin Editor. Whatever his other attitudes, Dr. Frankenstein knows that his creation suffers In his interesting article "Artificial Intelligence and Ethics: and knows it in the way he knows human beings suffer. An Exercise in the Moral Imagination," Michael LaChat Closely connected to this is the point that just as we have says that the basic outline of Shelley's Frmkensteirz needs to the moral obligation not to err on the wrong side in the fulfillment be recapitulated "even if, as is usually the case, the reader of our obligations toward suffering human beings (that has seen only the poor image of the book in movie form " is, WC should not fail to fulfill our obligations toward them Contrary to what Mr. LaChat says, I think the poor image just because we think they are not really suffering or, worse, most people have of the book is sufficient reason to give a because we haven't been able to prove that they are really short outline of the original story. Doing this, we find one or suffering), we ought not to err on the wrong side for the two arguments that were not mentioned in LaChat's article wrong reason in the case of suffering artifacts.

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 .

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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.

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. Two knowledge-based factory scheduling systems that implement aspects of this approach are described.