This paper describes techniques for representing control knowledge in preaicate calculus. A hybrid system is described in which metarules for diagnostic problem solving and their interpreter (both supplied by the NEOMYCIN program) are expressed in a form of predicate calculus (supplied by MRS). Procedural attachment is used to access and execute the untranslated domain knowledge. A simple deliberation/action loop manages the system at the highest level. There are three metalevels of reasoning.

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.

In this paper I take an empirical look at the question of whether there are rational memckis of discovery and claim that computer programs provida a laboratory for experimentation on this question Recent work in artificial intelligence or Al. has produced programs capaole of serious intellectual work in science Results from Al,viii be used to show that there exist mechanized procedures for discw.ering

The application of Artificial Intelligence techniques to real-world problems has produced promising research results, but seldom has a system becoma a useful tool in its domain of expertise.

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.

'ince much of cancer chemotherapy research this reasoning system with a high speed interface today is dependent upon rigorously designed and to help make the system acceptable to oncologists.

During the quarter century since the birth of "artificial intelligence" (Al), attempts to develop symbolic models of human reasoning processes have been a major focus of the ongoing research. It is only in the last half-dozen years or so, however, that several related Al research themes have come together in the formation of what is now known as "expert systems researoh" CI], In this brief paper I would 1.ke to review the key aspects of A: and expert syste-.s

Reserved Function Names 109 Appendix Ili.

Programs Based on the Blackboard Model 8 2.2.1.

"Learning", the incorporation of additional knowledge into expert systems, ranges from human data entry (learning by being told), to data gathering (learning by observing), to full-fledged theory formation (learning by discovery). One important kind of learning is the compiling of descriptive meta-knowledge into strategic form, recasting it into a form in which it can be evaluated efficiently. Much of what we earlier called strategic meta-knowledge may be seen to be operationalized "caches" of descriptive or systemic meta-knowledge. For instance, R9 and R10 can be converted from systemic to strategic form by slight rewordings of their actions.