A survey of early work exploring how AI can be used in medicine, with somewhat more technical expositions than in the complementary volume Artificial Intelligence in Medicine."Each chapter is preceded by a brief introduction that outlines our view of its contribution to the field, the reason it was selected for inclusion in this volume, an overview of its content, and a discussion of how the work evolved after the article appeared and how it relates to other chapters in the book.

This book has been adapted in large part from the author's doctoral thesis [Shortliffe, l 974b]. Portions of the work appeared previously in Computers And Biomedical Research [Shortliffe, 1973, l 975b], Mathematical Biosciences [Shortliffe, 1975a], and the Proceedings Of The Thirteenth San Diego Biomedical Symposium [Shortliffe, l 974a]. To Stanford's Medical Scientist Training Program, which is supported by the National Institutes of Health Contents

JANICE S. AIKINS Dr. Aikins received her Ph.D. in computer science from Stanford University in 1980. She is currently a research computer scientist at IBM's Palo Alto Scientific Center. She specializes in designing systems with an emphasis on the explicit representation of control knowledge in expert systems. ROBERT L. BLUM Dr. Blum received his M.D. from the University of California Medical School at San Francisco in 1973. From 1973 to 1976 he did an internship and residency in the Department of Internal Medicine at the Kaiser Foundation Hospital in Oakland, California, where he was chief resident in 1976.

For several years now, many members of the AI research community have expressed dissatisfaction with the paper review process for the National Conference on AI (AAAI). Accepted papers are almost universally written very conservatively, and many of the most interesting recent results have appeared in only specialty conferences, not at AAAI. The innovative, controversial papers that used to characterize the conference are getting harder and harder to find in the proceedings. Several efforts have been made by program chairs in recent years to improve the situation. For AAAI-93, an extensive effort was made to encourage reviewers to accept "innovative" papers.

I would like to add my support to Lawrence Hunter's proposal to modify the review process for the National Conference on Artificial Intelligence (NCAI). For some time now, I, too, have been disappointed with the majority of papers presented at NCAI-not with the quality of the papers but with the conservative style. I would leave a paper session thinking that AI is progressing but at a painstakingly slow pace! Someone, somewhere must be doing some really innovative research, but why isn't he or she presenting this work at the premier AI conference? Allowing controversial papers but maintaining the quality criteria is a needed improvement for NCAI and AI in general.

Design has long been an area of particular interest for AI researchers. Herbert Simon's 1968 Karl Taylor Compton lectures on the sciences of the artificial included substantial material on design. However, only recently have design researchers embraced paradigms from AI and AI researchers chosen design as a domain to study. Design research is a relatively new field, commencing in the 1960s with developments in design theories and methodologies. Although the results of the early design research produced domainindependent approaches to understanding and structuring design, the designers themselves were more comfortable with research that was specific to their own discipline.

In the lead article, Paul Cohen analyzes over 1.50 papers that were presented at the national conference last summer. Based on this analysis, he makes some interesting observations on the types of research in which we currently engage. Most research (or at least most research considered worthy of presentation by the AAAI-90 Program Committee) follows one of two strategies, according to Cohen's statistical analysis. One strategy is model oriented; that is, formal models of symbolic problem solving are hypothesized to be applicable to particular situations and then often tested on toy problems. The second strategy is system oriented; that is, it emphasizes the building of systems to solve difficult real-world problems.

Allen Newell 1927-1992 Allen Newell, one of the founders of the fields of artificial intelligence and cognitive science, died on July 19, 1992 in Pittsburgh, Pennsylvania. He was among the founders of AAAI and served as the association's first president.

In this paper we discuss a hybrid approach combining Case-Based Reasoning (CBR) .

A. YONEZAWA and C. HEWITT 41 REPRESENTATIONS FOR ABSTRACT REASONING 4 A maximal method for set variables in automatic theorem-proving W. W. BLEDSOE