Reprinted from the Proceedings of SIGPLAN 1983 Symposium on Programming Languages Issues in Software Systems, Data Abstraction in GLISP by Gordon S. Novak, Jr.

Report 82-33 Welcome to the MRS TUTOR!!! This tutor is designed to introduce you to the syntax and basic database accessing functions of MRS. This document is a transcript of an interaction with the MRS tutor. Reprinted by permission of the author. Funding for this work was provided by ONR Contract N00014-81-K-0004. Representation languages provide a way to store and retrieve facts from a computer. Since English is a grammatically and textually ambiguous language, representation systems use a more formal language to describe the world. The way in which the words or symbols of a language are put together to form phrases and sentences is termed the "syntax" or "grammar" of the language.

Data structures arc described to GEV using the GLISP Structure Description language III; if the user is already programming in GLISP.

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.

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

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

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.

List of Tables Table 4-1: Status determined based on the ''alues returned by selection rules 13 ACKNOWLEDGMENTS 1 would like to thank