A frame-based representation facility contributes to a knowledge system's A fundamental observation arising from work in artificial intelligence (AI) has been that expertise in a task domain requires substantial knowledge about that domain. Domain knowledge typically has many forms, including descriptive definitions of domain-specific terms (e.g., "power plant," "pump, " "flow," "pressure"), descriptions of individual domain objects and their relationships to each other ('e.g.,"Pl is a pump whose pressure is 230 psi"), and criteria for making decisions (e.g., "If the feedwater pump pressure exceeds 400 psi, then close the pump's input value"). Because of this emphasis on representatbon and domain knowledge, systems that use AI techniqules to achieve expertise are often referred to as knowledge-based systems, or simply as knowledge systems. In order for a knowledge system to use domainspecific knowledge, it must have a language for representing that knowledge. The predicate calculus was appealing because of its very general expressive power and well-defined se-. However, because the language constructs are very fine grained and do not provide adequate facilities for defining more complex constructs, domain experts have difficulty using the predicate calculus or understanding knowledge expressed in it.

Schlumberger is a large, multinational corporation concerned primarily with the measurement, collection, and interpretation of data. For the past fifty years, most of the activities have been related to hydrocarbon exploration. The efficient location and production of hydrocarbons from an underground formation requires a great deal of knowledge about the formation, ranging in scale from the size and shape of the rock's pore spaces to the size and shape of the entire reservoir. Schlumberger provides its clients with two types of information : measurements, called logs, of the petrophysical properties of the rock around the borehole, such as its electrical, acoustical, and radioactive characteristics; and in terpretations of these logs in terms of geophysical properties such as porosity and mineral composition. Since log interpretation is expert skill, the emergence of expert systems technology prompted Schlumberger's initial interest in Artificial Intelligence. Our first full- scale attempt at a commercial-quality expert system was the Dipmeter Advisor. Following these initial efforts, Schlumberger has expanded its Artificial Intelligence activities, and is now engaged in both basic and applied research in a wide variety of areas.

Canadians have made many contributions to artificial intelligence over the years. This article presents a summary of current research in artificial intelligence in Canada and acquaints readers with the Canadian organization for artificial intelligence -- the Canadian Society for the Computational Studies of Intelligence / Societe Canadienne pour l' Etude de l'Intelligence par Ordinateur (CSCSI/ SCEIO).

Artificial Intelligence is rapidly leaving its academic home and moving into the marketplace. There are few precedents for an arcane academic subject becoming commercialized so rapidly. But, genetic engineering, which recently burst forth from academia to become the foundation for the hot new biotechnology industry, provides useful insights into the rites of passage awaiting the commercialization of artificial intelligence. This article examines the structural similarities and dissimilarities in the two subjects and briefly summarizes the history of the commercialization of genetic engineering. It then proposes some lessons that would benefit the artificial intelligence industry.

This article surveys a portion of the field of natural language processing. The main areas considered are those dealing with representation schemes, particularly work on physical object representation, and generalization processes driven by natural language understanding. The emphasis of this article is on conceptual representation of objects based on the semantic interpretation of natural language input. Six programs serve as case studies for guiding the course of the article. Within the framework of describing each of these programs, several other programs, ideas, and theories that are relevant to the program in focus are presented.

In the past fifteen years artificial intelligence has changed from being the preoccupation of a handful of scientists to a thriving enterprise that has captured the imagination of world leaders and ordinary citizens alike. While corporate and government officials organize new projects whose potential impact is widespread, to date few people have been more affected by the transition than those already in the field. I review here some aspects of this transition, and pose some issues that it raises for AI researchers, developers, and leaders.

Abstract, Editors' Note: In this provocative article Doyle suggests that many of the benefits of current expert systems technology Knowledge engineers qualified to build expert systems are currently in could be achieved without computer-based implementations. Is there not an intermediary position? This revolution is very Namely, that the problems encountered by today's expert important. The views and conclusions contained manpower. The novice still botches the task, but explains in detail of knowledge engineers in the current fashion.

AI technology transfer Is the diffusion of AI research techniques into commercial products. I have been involved in this process since 1975, when the Artificial Intelligence Corporation began to develop ROBOT, the prototype of INTELLECT, a commercially viable natural language interface to data base systems which has been on the market since 1981. In this article, I will discuss AI technology transfer with particular reference to my experiences with the commercialization of INTELLECT. I will begin with the historical perspective of where the field of AI came from, where it is now, and where it is going. Next, I will describe my interpretation of the present market structure for AI products and some specific marketing perspectives. I will then briefly describe the product INTELLECT and its capabilities as an example of a state-of-the-art commercial system. Next, I will describe some of the experiences, which I think are typical, that my company has encountered in commercialize their systems.

Many techniques for representing knowledge have been proposed, but there have been few reports that compare their application. This article presents an experimental comparison of four knowledge representation schemes: a simple production system, a structured production system. A frame system, and a logic system. We built four pilot expert systems to solve the same problem: risk management of a large construction project. Observations are made about hoe the structure of the domain knowledge affects the implementation of expert systems and their run time efficiency.

Most work in automatic programming has focused primarily on the roles of deduction and programming knowledge. However, the role played by knowledge of the task domain seems to be at least as important, both for the usability of an automatic programming system and for the feasibility of building one which works on non-trivial problems. This perspective has evolved during the course of a variety of studies over the last several years, including detailed examination of existing software for a particular domain (quantitative interpretation of oil well logs) and the implementation of an experimental automatic programming system for that domain. The importance of domain knowledge has two important implications: a primary goal of automatic programming research should be to characterize the programming process for specific domains; and a crucial issue to be addressed in these characterizations is the interaction of domain and programming knowledge during program synthesis.