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.

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. 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.

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 prototype system is written in Prolog, a language that has proved to be very powerful and easy to use for problem /rule development. The resulting prototype system (called EXPRS-Expert Prolog System) uses English-like rule constructs of Prolog code. This approach enables the system to generate answers automatically to "why" a ruled fired, and "how" that rule fired. In addition, a rule clause construct is provided which allows direct access to Prolog code routines.

During the past year, a prototype expert system for tactical data fusion has been under development,. This computer program combines various messages concerning electronic intelligence (ELINT) to aid in decision making concerning enemy actions and intentions. The prototype system is written in Prolog, a language that has proved to be very powerful and easy to use for problem /rule development. The resulting prototype system (called EXPRS-Expert Prolog System) uses English-like rule constructs of Prolog code. This approach enables the system to generate answers automatically to "why" a ruled fired, and "how" that rule fired. In addition, a rule clause construct is provided which allows direct access to Prolog code routines. This paper describes the structure of the rules used and provides typical user interactions.

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.

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.

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.

Over the past few years, the character of the AI community has changed. AI researchers used to be able to go about their work in peace, while the rest of the world ignored them. As the promise of partical applications of AI has slowly become reality, new players have entered the field, changing its nature forever. The quiet, intellectual community of AI researchers has been augmented by a hoard of other interested parties, including the press, the financial community, and the technology entrepreneurs. Since we cannot go back and hide in the ivory tower, we may as well take the time to explore our new environment. I invite you to join me in a guided tour of the new AI community. Let's begin by analyzing the basic motivation for commercial interest in AI.

How to Get the Most Out of IJCAI-83 Vol IV, No 1, p 35, Winter-Spring, 1983.