This article reviews the strengths and limitations of five major paradigms of medical computer-assisted decision making (CAOM): 1) clinical algorithms.

This paper describes a method of cutting off reasoning when all of the answers to a problem have been found. Briefly, the method involves keeping and maintaining information about the sizes of important sets.

Georgeff's controlled production systems 65 8.3.3

These diverse roles impose several requirements consultation programs has become known as »expert systems upon the system.

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.

The first computers were numerical calculators that did not appear to embody much intelligence at all. But before these machines were even designed.

MRS is a knowledge representation systmt intended for use by Al researchers in building expert systems. It offers a diverse repertory of commands for asserting and retrieving information, with various representatiuns (e.g. The initial system includes a vocabulary of concepts and facts about logic, sets, mappings, arithmetic, and procedures. What differentiates MRS from many other knowledge representation systems is its ability to observe, reason about, and control its own activity. In MRS the system is treated as a domain in its own right.

Artificial intelligence, long a topic of basic computer science research, is now being applied to problems of scientific, technical, and commercial interest. Some consultation programs, though limited in versatility, have achieved levels of performance rivaling those of human experts. A collateral benefit of this work is the systematization of previously unformalized knowledge in areas such as medical diagnosis and geo!ogy.

I cis is the final report for Contract N-00014-79-C-03C2, covering the period of 15 March 1979 through 14 March 1982. The goal of the project was to develop methods for representing teaching and problem-solving knowledge in computer-based tutorial systems. One focus of the work was formulation of principles for managing a case method tutorial dialogue; the other major focus was investigation of the use of a production rule representation for the subject material of tutorial program. The main theme pursued by this research is that representing teaching and problemsolving knowledge separately and explicitly enhances the ability to build, modify and test complex tutorial programs. Two major corr Jter programs were constructed.

During the quarter century since the birth of the branch of computer science known as artificial intelligence (Al), much of the research has focused on developing symbolic models of human inference. In the last decade several related Al research themes have come together to form what is now known as "expert systems research."1 In this paper we review Al and expert systems to acquaint the reader with the field and to suggest ways in which this research will eventually be applied to advanced medical monitoring.