I suspect that their motive was summary 3. the agent can justify its belief that s is true, i e., rejection of newfangled techniques, not eymological In any case I answered them. Perhaps your readers may be interested in the knows s]). An agent's problem-solving behavior is intelligent if (and to the extent that) the agent's problemsolving Sincerely yours, An agent's problem-solving behavior is artificially intelligent if the behavior is intelligent and the Definition 1. An agent lcnows some statements if the agent is a machine. Remark: It is a consequence of this definition of artificial 1. s is true, i.e, s is either a logical truth (a theorem intelligence that artificial intelligence does not equal artificial or a tautology) or a factual truth (a correspondence endocrinology! with fact);

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.

AI technology transfer Is the diffusion of AI research techniques into commercial products. In this article, I will discuss AI technology transfer with particular reference to my experiences with the commercialization of INTELLECT. 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.

Artificial intelligence (AI) will have profound societal effects. It promises potential benefits (and may also pose risks) in education, defense, business, law and science. In this article we explore how AI is likely to affect employment and the distribution of income. We argue that AI will indeed reduce drastically the need of human toil.

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.

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.

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. 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. I invite you to join me in a guided tour of the new AI community.

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.

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.