Probability concepts for ruled-based expert systems are developed that are compatible with probability used in data fusion of imprecise information. Procedures for treating probabilistic evidence are presented, which include the effects of statistical dependence. 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. Object-oriented computing provides a principle for unifying these different methodologies within a single system.

Originally it was Complex Information Processing. That was the name Herb Simon and I chose in 1956 to describe the area in which we are working. It didn't take long before it became Artificial Intelligence (AI). Coined by John McCarthy, that term has stuck firmly, despite continual grumblings that any other name would be twice as fair (though no grumblings by me; I like the present name). Complex Information processing lives on now only in the title of the CIP Working Papers, a series started by Herb Simon in 1956 and still accumulating entries (to 447). However, from about 1965 much of the work on artificial intelligence that was not related to psychology began to appear in technical reports of the Computer Science Department. These reports, never part of a coherent numbered series until 1978, proliferated in all directions. Starting in the early 1970s (on one can recall exactly when), they did become the subject of a general mailing and thus began to form what everyone thinks of as the CMU Computer Science Technical Reports.

In 1980, Digital Equipment Corporation began to use a rule-based system called R1 by some and XCON by others to configure VAX-11 computer systems. In the intervening years, R1's knowledge has increased substantially and its usefulness to Digital continues to grow. This article describes what is involved in extending R1's performance during the four year period.

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.

The state of the art in any science includes the criteria for evaluating research. Like every other aspect of the science, it An example is the alpha-beta heuristic for game playing. The criteria for evaluating AI research Humans use it, but it wasn't identified by the writers of the are not in very good shape. I had intended to produce four first chess programs. It doesn't constitute a game playing presidential messages during my term but have managed only program, but it seems clearly necessary, because without two, because this one has proved so difficult to write.

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.

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.

Founded early in 1983, the center for the Study of Language and Information (CSLI) at Stanford University grew out of a long-standing collaboration between scientists at research laboratories in the Palo Alto area and the faculty and students of several Stanford University departments and out of a need for an institutional focus for this work on natural and computer languages. At present, CSLI has 17 senior members and about as many associate members, from SRI International, Xerox PARC, Fairchild, and the Department of Computer Science, Linguistics, and Philosophy at Stanford. Since the Center's research will overlap with the work of other researchers around the world, an important goal of CSLI is to initiate a major outreach, whereby members of CSLI both inform themselves of work done elsewhere and share their own results with others.