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.

Over the past few years, the notion of a "prototype" (e.g., TYPICAL-ELEPHANT) seems to have caught on securely in knowledge representation research. Along with a way to specify default properties for instances of a description, proto-representations allow overriding, or "canceling" of properties that don't apply in particular cases. This supposedly makes representing exceptions ( three-legged elephants and the like ) easy; but, alas, it makes one crucial type of representation impossible-that of composite descriptions whose meanings are functions of the structure and interrelation of their parts. This article explores this and other ramifications of the emphasis on default properties and "typical" objects.

The fact was there were a lot of failures. There I have been assigned the role of survivalist. First I want to were overruns and systems delivered past schedule. This ask, "Has AI paid its way?"... Or to put it another way, is certainly not unique to Naval Electronic System Command. "Have we earned our keep?" I have three answers to that: The most would be systems being acquired for the Yes, yes, and yes.

The following article briefly describes the development of tools and knowledge in human history and states that these two phenomena co-exist only in intelligent tolls. It focuses on the productive merits of the past intelligent tools and discusses the social and biohuman). Moreover, since the human beings were unable to produce an intelligent tool capable of outperforming man as a tool, the tech nological basis of slavery continued to persist throughout history. The article then examines the current achievements of computer technology in producing intelligent tools. It argues that the production of intelligent tools makes it possible to bypass the social and natural limitations of all past intelligent tools. Once these tools outperform humans as intelligent tools, man will no longer be indispensable as a production tool. Consequently, the inception of these new tools eradicates the technological basis of the subjugation of man by man. This eradication may start a new civilization by effecting higher human intelligence, more economic wealth and greater socio-political freedom in man's future society.

The classical approach to the acquisition of knowledge and reason in artificial intelligence is to program the facts and rules into the machine. Unfortunately, the amount of time required to program the equivalent of human intelligence is prohibitively large. An alternative approach allows an automaton to learn to solve problems through iterative trial-and-error interaction with its environment, much as humans do. To solve a problem posed by the environment, the automaton generates a sequence or collection of responses based on its experience. The environment evaluates the effectiveness of this collection, and reports its evaluation to the automaton. The automaton modifies its strategy accordingly, and then generates a new collection of responses. This process is repeated until the automaton converges to the correct collection of responses. The principles underlying this paradigm, known as collective learning systems theory are explained and applied to a simple game, demonstrating robust learning and dynamic adaptivity.

Research in AI within the Computer Science Department at the University of California, Los Angeles is loosely composed of three interacting and cooperating groups: (1) the Artificial Intelligence Laboratory, at 3677 Boelter Hall, which is concerned mainly with natural language processing and cognitive modelling, (2) the Cognitive Systems Laboratory, at 4731 Boelter Hall, which studies the nature of search, logic programming, heuristics, and formal methods, and (3) the Robotics and Vision Laboratory, at 3532 Boelter Hall, where research concentrates on robot control in manufacturing, pattern recognition, and expert systems for real-time processing.

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.

It has been a gratifying experience to observe and to One major exception: I know that many members participate in the growth of our Association's Magazine. As the official publication worried about how we'd get enough material to put out the Moreover, there is no articles full, and I was concerned that it just be nonempty! We don't have that I had rejected almost nothing since I had taken over-the editorial staff to do extensive rewriting or editing. "All the news we get we print" was close to the truth. Most of the magazine published four issues, averaging a little over 40 pages each.

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.

Artificial Intelligence Research at the University of Michigan