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 .

Letters to the Editor

Editorial

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.

Editorials marking the first time AI Magazine has been devoted to a single theme (the application of AI to manufacturing problems). Editorials marking the first time AI Magazine has been devoted to a single theme (the application of AI to manufacturing problems).

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 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 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.