"Expert systems are among the most exciting computer applications to emerge in the last decade. They allow a computer program to use expertise to assist in a variety of problems, such as diagnosing equipment failures and designing new equipment. Utilizing the results of artificial intelligence (AI) work on problem solving, they have become a commercially successful demonstration of the power of AI techniques. Correspondingly, by testing current AI methods in applied contexts, expert systems provide important feedback to the science about the strengths and limitations of these methods. In this review, we present the fundamental considerations in constructing an expert system, assess the state of the art, and indicate directions for future research. Our discussion focuses on the computer science issues, as opposed to issues of management or application." Annual Review of Computer Science.

In Marcus, S., (Ed.), Automating Knowledge Acquisition for Knowledge Based Systems, chapter 8, pages 120-146. Boston: Kluwer Academic Publishers

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A number of tools that support the development, execution, and maintenance of knowledge-based systems are marketed commercially. Many of these tools, however, are designed for applications that can be executed on personal computers and are not suitable for building large knowledge-based systems. The market for knowledge engineering tools designed for applications that require the computational power of a Lisp machine or an engineering workstation is dominated by a few vendors. This article is an assessment of the current state of tools used to build large knowledge-based systems.

VT: An Expert Elevator Designer That Uses Knowledge-Based Backtracking

The annual conference of the Association for the Advancement of Artificial Intelligence (AAAI) is the largest and most important meeting of AI theoreticians and practitioners in the United States. This year, the conference was held in Seattle, Wash., and paid attendance was just under 5100. Last year's Philadelphia conference drew 5400. The drop in attendance was primarily the result of competition with the International Joint Conference on Artificial Intelligence, which took place in Milan a few weeks after AAAI.

Sargur N. Srihari is a professor and acting chairman of the Department of Computer Science, State University of New York (SUNY) at Buffalo. The author of "Recognizing Address Blocks on Mail Pieces," Srihari is an associate editor of the journal Pattern Recognition and is chairman of the technical committee on text-processing applications of the International Mike Baird, who coauthored the tribute to Kvetoslav Prazdny, is manager of Association for Pattern Recognition. Srihari is also currently directing two Intelligence Center 1185 Coleman Avenue, Santa Clara, California 95052. Jeffrey Stone is a consultant who watches the computer industry and Jonathan J. Hull is a research assistant Digital Equipment Corporation that reports new developments and trends. The opinions expressed "Recognizing Address Blocks on Mail address is Knowledge Systems Corporation, in his article are his own. Jeffrey Stout is on the research staff of computer vision, and artificial intelligence. An Expert Elevator report on AI and education, is an Buffalo, where he is also currently Designer that Uses Knowledge-Based associate professor in the Department working on his Ph.D. His research Backtracking." of Mathematics and Computer Science interests include image processing, at Millersville University, computer graphics, and computer segmentation Jay M. Tenenbaum, who coauthored Millersville, Pennsylvania 1755 1. Palumbo is a the tribute to Kvetoslav Prazdny, is a coauthor of "Recognizing Address Schlumberger Fellow at the Schlumberger John McDermott is a principal scientist Blocks on Mail Pieces."

VT (vertical transportation) is an expert system for handling the design of elevator systems that is currently in use at Westinghouse Elevator Company. Although VT tries to postpone each decision in creating a design until all information that constrains the decision is known, for many decisions this postponement is not possible. In these cases, VT uses the strategy of constructing a plausible approximation and successively refining it. VT uses domain-specific knowledge to guide its backtracking search for successful refinements. The VT architecture provides the basis for a knowledge representation that is used by SALT, an automated knowledge-acquisition tool. SALT was used to build VT and provides an analysis of VT's knowledge base to assess its potential for convergence on a solution.

AI research are discussed This article is individuals outside the field. Even Of course, linguists have never an introduction to Scientific DataLink's AI'S practitioners are somewhat confused thought of their field as having much microfiche publication of the Yale AI about what AI really is. to do with AI at all. However, as technical reports In this context, examples Is AI mathematics? A great many money for linguistics has begun to of research conducted at the Yale AI researchers believe strongly that disappear and money for AI has Artificial Intelligence Project relating to knowledge representations used in AI increased, it has become increasingly each of the research problems is presented. Suddenly, theories of know how the answer will turn out language that were never considered even before they have figured out by their creators to be process models what exactly the questions are. They at all are now proposed as AI models.

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