Many of the ideas behind object-oriented programming have roots going back to SIMULA. The first substantial interactive, display-based implementation was the SMALLTALK language. The object-oriented style has often been advocated for simulation programs, systems programming, graphics, and AI programming. The history of ideas has some additional threads including work on message passing as in ACTORS, and multiple inheritance as in FLAVORS. It is also related to a line of work in AI on the theory of frames and their implementation in knowledge representation languages such as KRL, KEE, FRL, and UNITS.

One day a man, who had lost Using an ordinary text-editing algorithm and a variety of much of his long-term episodic memory, consulted the professor changeable key words, the man could call up stories on his to ask him if there was any way he could help him personal computer, read them aloud, and thus attempt to regain the lost memories. Being righthanded text-editing method is trivial, but this is not an article and left-hemisphere specialized for language, he about method; it is about ethics.) The hope was that was still able to speak, to read and write: and to understand not only would the man now have some memory to think what was said to him. Besides the usual difficulty about and talk about but, more importantly, this repeated in recalling proper names, his main problem involved large daily practice at his own pace, with no one looking over gaps in his memory for events that he participated in before his shoulder, might help open up new access paths to his the stroke, although he could remember events that own memory of these events, filling them in and modifying occurred after the stroke. He could not, however, remember the award out the plan.

The major limitations in building large software have always been (a) its brittleness when confronted by problems that were not foreseen by its builders, and (by the amount of manpower required. The recent history of expert systems, for example highlights how constricting the brittleness and knowledge acquisition bottlenecks are. Moreover, standard software methodology (e.g., working from a detailed "spec") has proven of little use in AI, a field which by definition tackles ill- structured problems. How can these bottlenecks be widened? Attractive, elegant answers have included machine learning, automatic programming, and natural language understanding. But decades of work on such systems have convinced us that each of these approaches has difficulty "scaling up" for want a substantial base of real world knowledge.

During the balmy summer of 1980, about 800 AI researchers pose of the new format, the Committee's recommendation, met on the Stanford campus to hold the first and some expanded ways for members to participate in the AAAI conference. The conference program had no more conference this year. For many of Conference Goals those attendees, it was a special, unique opportunity to have deep colleagial interactions in a very comfortable setting. The most radical change that was considered, but not adopted, was the division of the science and engineering interests into two separate conferences at different times of Even the first national conference, however, was more the year. Many Council members expressed concern that than a gathering of researchers.

This article explores the implications of one type of cognitive technology, techniques and concepts to develop joint human-machine cognitive systems, for the application of computational technology by examining the joint cognitive system implicit in a hypothetical computer consultant that outputs some form of problem solution. This analysis reveals some of the problems can occur in cognitive system design-e.g., machine control of the interaction, the danger of a responsibility-authority double-bind, and the potentially difficult and unsupported task of filtering poor machine solutions. The result is a challenge for applied cognitive psychology to provide models, data, and techniques to help designers build an effective combination between the human and machine elements of a joint cognitive system.

ECAI, Pisa 1984) to facilitate the decompiling of wealth and power.

General Electric is engaged in a broad range of research and development activities in artificial intelligence, with the dual objectives of improving the productivity of its internal operations and of enhancing future products and services in its aerospace, industrial, aircraft engine, commercial, and service sectors. Many of the applications projected for AI within GE will require significant advances in the state of the art in advanced inference, formal logic, and architectures for real-time systems. New software tools for creating expert systems are needed to expedite the construction of knowledge bases. Further, new application domains such as computer -aided design (CAD), computer- aided manufacturing (CAM), and image understanding based on formal logic require novel concepts in knowledge representation and inference beyond the capabilities of current production rule systems. Fundamental research in artificial intelligence is concentrated at Corporate Research and Development (CR&D), with advanced development and applications pursued in parallel efforts by operating departments. The fundamental research and advanced applications activities are strongly coupled, providing research teams with opportunities for field evaluations of new concepts and systems. This article summarizes current research projects at CR&D and gives an overview of applications within the company.

Tsukuba Expo '85 is huge, interesting, and fun. The Japanese pavilions are plush and well -organized and contain some impressive artificial intelligence demonstrations. The U.S. pavilion is an embarrassment.

The enterprise of developing knowledge-based systems is currently witnessing great growth in popularity. The central unity of many such programs is that they interpret knowledge that is explicitly encoded as rules. While rule-based programming comes with certain clear pay-offs, further fundamental advances in research are needed to extend the scope of tasks that can be adequately represented in this fashion. This article is a statement of personal perspective by a researcher interested in fundamental issues in the symbolic representation and organization ok knowledge.

Getting started on a new knowledge engineering project is a difficult and challenging task, even for those who have done it before. For those who haven't, the task can often prove impossible. One reason is that the requirements-oriented methods and intuitions learned in the development of other types of software do not carry over well to the knowledge engineering task. Another reason is that methodologies for developing expert systems by extracting, representing, and manipulating an expert's knowledge have been slow in coming. At Tektronix, we have been using step-by-step approach to prototyping expert systems for over two years now. The primary features of this approach are that it gives software engineers who do not know knowledge engineering an easy place to start, and that it proceeds in a step-by-step fashion from initiation to implementation without inducing conceptual bottlenecks into the development process. This methodology has helped us collect the knowledge necessary to implement several prototype knowledge-based systems, including a troubleshooting assistant for the Tektronix FG-502 function generator and an operator's assistant for a wave solder machine.