In the past twenty years, much time, effort, and money has been expended on designing an unambiguous representation of natural language to make them accessible to computer processing, These efforts have centered around creating schemata designed to parallel logical relations with relations expressed by the syntax and semantics of natural languages, which are clearly cumbersome and ambiguous in their function as vehicles for the transmission of logical data. Understandably, there is a widespread belief that natural languages are unsuitable for the transmission of many ideas that artificial languages can render with great precision and mathematical rigor. Among the accomplishments of the grammarians can be reckoned a method for paraphrasing Sanskrit in a manner that is identical not only in essence but in form with current work in Artificial Intelligence. This article demonstrates that a natural language can serve as an artificial language also, and that much work in AI has been reinventing a wheel millenia old.

A pattern recognition algorithm is described that learns a transition net grammar from positive examples. Two sets of examples -- one in English and one in Chinese -- are presented. It is hoped that language learning will reduce the knowledge acquisition effort for expert systems and make the natural language interface to database systems more transportable. The algorithm presented makes a step in that direction by providing a robust parser and reducing special interaction for introduction of new words and terms.

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. 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.

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.

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. It then proposes some lessons that would benefit the artificial intelligence industry.

Probability concepts for ruled-based expert systems are developed that are compatible with probability used in data fusion of imprecise information. Confidence limits are defined as being proportional to root-mean-square errors in estimates, and a method is outlined that allows the confidence limits in the probability estimate of the hypothesis to be expressed in terms of the confidence limits in the estimate of the evidence. Procedures are outlined for weighting and combining multiple reports that pertain to the same item of evidence. The illustrative examples apply to tactical data fusion, but the same probability procedures can be applied to other expert systems.

This article describes our initial experience with building applications programs in a hybrid AI tool environment. Traditional AI systems developments have emphasized a single methodology, such as frames, rules or logic programming, as a methodology that is natural, efficient, and uniform. The applications we have developed suggest that natural-ness, efficiency and flexibility are all increased by trading uniformity for the power that is provided by a small set of appropriate programming and representation tools. The tools we use are based on five major AI methodologies: frame-based knowledge representation with inheritance, rule-based reasoning, LISP, interactive graphics, and active values.

At Vanderbilt University we are exploring the use of expert systems in a broad range of application areas. Programming is in Franz Lisp on a VAX 11/790, UCI LISP on a DEC-10, and IQ LISP on an IBM XT. Currently, personnel from four schools in the University are participating. Listed are brief descriptions of current projects.

Knowledge engineers qualified to build expert systems are currently in short supply. The production of useful and trustworthy expert systems can be significantly increased by pursing the idea of articulate apprenticeship independent of computer implementations. Making theoretical progress in artificial intelligence should also help.

The Steamer project is a research effort concerned with exploring the use of AI software and hardware technologies in the implementation of intelligent computer-based training systems. While the project addressed a host of research issues ranging from how people understand complex dynamic systems to the use of intelligent graphical interfaces, it is focused around the construction of a system to assist in propulsion engineering instruction. The purpose of this article is to discuss the underlying ideas which motivated us to initiate the Steamer effort, describe the current status of the project, provide a glimpse of our planned directions for the future, and discuss the implications of Steamer for AI applications in other instructional domains.