Probably, it is apt to conclude this discussion on a lighter note. To quote McDermott again, "To say anything good about anyone is beyond the scope of this letter." The Price Waterhouse worldwide financial services business provides a wide range of opportunities for using knowledge-based systems technology. In order to be realized, many of those opportunities require significant advances in the underlying technologies. The Price Waterhouse Technology Centre provides an environment in which high quality research and development professionals can pursue those advances and explore the strengths and weaknesses of the resulting technology by using it to build prototype systems that address central problems in the firm's business.

Artificial Intelligence for Microcomputers If you would like to develop an expert system or knowledgebased system on a microcomputer, you might want to read Artijcial Intelligence for Microcomputers by Mickey Williamson, This nontechnical book is easy to understand, written for the unsophisticated microcomputer user. The first chapters provide a brief history of artificial intelligence (AI) and an introduction to natural language query systems. They explain what knowledge-based systems and expert systems are and how they work. Discussions are also provided of the two major AI programming languages, Lisp and Prolog, including their strengths and weaknesses. The remainder of the book is devoted to a review of some of the existing AI software products for microcomputers, such as natural language query systems, decision support systems, expert system development shells, and AI programming languages.

A five-year project, it is aimed at promoting cooperation among research centers, developing state-of-the art training and teaching programs, and demonstrating KBCS solutions to selected socioeconomic problems. The Department of Electronics, Government of India, with the assistance of the United Nations Development Program (UNDP) decided in 1986 to support a five-year project on knowledge-based computer systems (KBCSs). Seven major research and teaching centers and a number of associated institutions are involved in the project. The nodal centers in this project are the Center for the Development of Advanced Computing (Pune), the Department of Electronics (New Delhi), The Indian Institute of Science (Bangalore), The Indian Institute of Technology (Madras), the Indian Statistical Institute (Calcutta), the National Center for Software Technology (Bombay), and the Tata Institute of Fundamental Research (Bombay). The objectives are multiple but among them are to build an institutional infrastructure and promote cooperation among research centers in India; develop state-of-the-art training and teaching programs; and demonstrate specific KBCS solutions to selected socioeconomic problems encountered, in particular, in India.

The PROSENET/TEXTNET approach is designed to facilitate the generation of polished prose by an expert system. The approach uses the augmented transition network (ATN) formalism to help structure prose generation at the phrase, sentence, and paragraph levels. The approach also uses expressive frames to help give the expert system builder considerable freedom to organize material flexibly at the paragraph level. The PROSENET /TEXTNET approach has been used in a number of prototype expert systems in medical domains, and has proved to be a convenient and powerful tool. One component of this interface for many systems involves the generation of English prose to communicate the expert system's conclusions and recommendations.

Artificial Intelligence Department, Computer Resenrch Laboratory, Tektronix, 1, Post Office Box 500, Beaverton, Oregon 97077 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 requirementsoriented 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 a step-by-step approach to prototyping expert systems for over two years now.

Probability concepts for rule-baaed 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. These programs use a collection of facts, rules of thumb, and other knowledge about a limited field to help make inferences in the field. They differ substantially from conventional computer programs in that their goals may have no algorithmic solution, and they must make inferences based on incomplete or uncertain information.

A Nonmonotonic Inference Engine People are often forced by circumstances to make judgments based on incomplete information. These circumstances do not disappear when we augment our native reasoning ability with the use of knowledge bases and automated reasoning systems. It is therefore extremely important that our systems be able to assist us in this kind of reasoning. Frequently, the best conclusion that can be drawn from an incomplete set of facts about a situation are different from the best conclusion that can be drawn from a complete or nearly complete superset of the same facts. The set of conclusions we draw as our information increases does not simply change in one direction or monotonically by getting larger; it can also shrink as our previous best conclusions are rejected on the basis of new information.

The following letter was addressed to Daniel Bobrow, editor of Artificial Intelligence Many of us felt that the issue raised is a very important one for the AAAI and deserved wide exposure It is printed here, along with Bobrow's reply, for your interest. The intervening three centuries have proven Oldenburg's invention to be a priceless vehicle for the dissemination of knowledge. It is, therefore, ironic indeed that artificial intelligence, a field whose very essence is knowledge, has developed a literature that is extraordinarily difficult and inefficient to use. Effective use of the literature of AI is frustrated by two fundamental deficiencies: (a) there is no central index to the field's published works, and (b) not only are far too many original works not published in journals, but a shockingly high percentage of these are "published" in sources that may be generously described as inaccessible. As an example of a field that does not have these problems, consider medicine.

The Center for Automation and Intelligent Systems Research at Case Western Reserve University, founded in 1984, provides the setting and the administrative and funding mechanisms for coordinating and focusing the capabilities of faculty members and students from many disciplines and departments to deal with significant realworld problems encountered in the automation of production. The center serves as an interface between separate basic research efforts in the various disciplines and academic departments and the multidisciplinary group efforts needed to deal effectively with nontrivial real problems. The main focus of research at the center is on the effective integration of computer-based technologies that appear to be essential for the factory of the future. Thus, the scope of activities at the center is somewhat broader based than AI, but AI plays a central role in this integration because effective integration of complex systems requires intelligence. The major emphasis at the center is on industrial applications.

Introduction This report summarizes our experience in building PIES, a knowledge-based system that diagnoses problems in semiconductor fabrication processes by analyzing parametric test data. Parametric measurement, which is performed on test circuits at the end of a complicated semiconductor fabrication process, provides semiconductor engineers with early information to monitor the "health' ' of the overall fabrication process. Typically, hundreds of measurements are made on each wafer. The problem is to reduce the resulting ream of data to a concise summary of the process status: whether the process is functioning correctly and, if not, what the nature and cause of the abnormality is. Currently, this interpretation taskis performed by a group of semiconductor specialists known as failure-analysis or yield-enhancement engineers and routinely consumes a large portion of their time. It is critical that problems be identified quickly to avoid a major operational loss.