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.

Edward H. Shortliffe Chapter 6 Details of the Revised Therapy Algorithm

In this section, I propose another, quite different view about the nature To choose their actions, reasoning programs must be able to make assumptions and subsequently of reasoning. I incorporate some new concepts into this view, and the combination revise their beliefs when discoveries contradict these assumptions. The Truth Maintenance System overcomes the problems exhibited by the conventional view.

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

Meta-level control, in an Artificial Intelligence system, can provide increased capabilities and improved performanct. This improvement, however, is achieved at the cost of the meta-level effort itself. To ensure an overall increase in system efficiency, the savings brought about at the base level cannot be exceeded by the effort at the meta-level. This paper outlines a formalization of the costs involved in choosing between independent problem-solving methods: the cost of meta-level control is explicitly included. It is shown that when meta-level effort is related to its efficacy, there exists an amount of this effort that should optimally be expended. Too much or too little meta -le-,e1 effort can result in a loss of overall system performance.

The Stanford University component of this research is funded in part by ARPA contract #MDA903-80-C-0107, NIH contract # NIH RR 00785-10, ONR contract #N00014-79-C-0302. Compiled oy Bruce G. Buchanan November 1982 Abbreviations Used in This Bibliography: AAAI American Association for An:ficial Intelligence ACM Association for Computing Machinery AFIPS American Federation of Information Processing Societies ECAI European Conference on Artificial Intelligence IEEE Institute for Electrical and Electronic Engineers IFIPS International Federation of Information Processing Societies IJCAI International Joint Cr nferences on Artificial Intelligence SIGPLAN ACM Specia! Abe, N., ltoh, F., and Tsuji, S. Toward a learning of object models using analogical objects and verbal instruction. Addis, T. R., and Hartley, R. T. A faultfinding aid u,sing a content addressable file store. ICL Technical Note TN 79, ICL Ltd., London, 1979.

They do not necessarily reflect those of the Xerox Corporation, Stanford University, or the University of Southern California. This study was funded in part through the SUMEX Computer Project at Stanford University under grant 1212-007R5 from the Biotechnology Resources Program of the National Institutes of Health. I. INTRODUCTION Since November 1979, a group at the Information Sciences Institute of the University of Southern California has been working on ell implementation of Interlisp for the DEC VAX-scries1 computers. 'Ibis report is a description of the current status, future prospects, and estimated character of that Interlisp-VAX implementation. It is the result of several days of discussion with those at ISI involved with the implementation (Dave Dyer, Hans Koomen, Ray Bates. Dan Lynch); with John L. White of MIT, who is working on an implementation of another Lisp for the VAX (NIL); with the implementors of Interlisp-Jericho at 1311N (Alice Hartley, Norton Grecnfeld, Martin Yonkc, John Vittal, Frank Zdybel, Jeff Gibbons, Wryle Lewis); with the implementors of Franz Lisp and Berkeley Unix2 at U.C. Berkeley (Richard Fateman, Bill Joy, Keith Sklower, John Foderaro); and with my colleagues at Xerox PARC. An earlier draft of this report was circulated to the parties involved in the Interlisp-VAX discussions. 'Ibis document has been revised as a result of comments received.

Abstractions include not only small, simple concepts like hierarchies but also more complex notions like concavity and convexity or particles and waves. A model for an abstraction is essentially an interpretation for the symbols that satisfies the associated axioms. Different task domains can be models of the same abstraction (as biological taxonomy, geological time, and organization charts arc instances of hierarchies): or, said the other way around.