The general practitioner has accordingly become rare, and today's primary care physicians are beginning to graduate from family practice residencies which recognize that "family doctoring" is a subspecialty in itself. Thus when a patient's problem clearly falls outside the area of the attending physician's expertise, consultations from experts in other subspecialties have become a wellaccepted part of medical practice. Such consultations are acceptable to doctors in pert because they maintain the primary physician's role as ultimate decision maker. The consultation generally involves a dialog between the two physicians, with the expert explaining the basis for his advice and the nonexpert seeking jus-MYCIN Project is located at Stanfcrd University School of Medicine and is zepported by BHSRE Grant No. HS01544. Much of the work described in this report yes undertaken by other project members, notably A.C. Scott and W.J. Clancey, who have devoted much of their time to improvements in the general question-answerer, and R. Davis, who did most of the work on the reasoning status checker and on knowledge acquisition capabilities.

A new predicate calculus deduction system based on production rules is proposed. The system combines several developments in Artificial Intelligence and Automatic Theorem Proving research including the use of domain-specific inference rules and separate mechanisms for forward and backward reasoning. It has a clean separation between the data base, the production rules, and the control system. Goals and subgoals are maintained in an AND/OR tree to represent assertions. The production rules modify these structures until they "connect" in a fashion that proves the goal theorem. Unlike some previous systems that used production rules, ours is not limited to rules In Horn Clause form. Unlike previous PLANNER-like systems, ours can handle the full range of predicate calculus expressions including those with quantified variables, disjunctions and negations.

Because of the recent - echnological advances in computer networks and communications, and because of the cost reduction of computer hardware, there has been a great interest in distributed data bases including some attempts at actual implementations. In this paper, we will first define what we mean by a distributed data base. Then we will give some of the reasons why people are so interested in this new field. After classifying the different types of distributed data bases, we will describe the current areas of research. Finally, we will give an annotated bibliography that lists the most important papers in thi:3 area.

Stanford KSL of Candidate Structures, Based on Comparison between Predicted and Observed Mass Spectra. Mass spec'ral data may be used in structural studies in several ways: 1) For examp:.e, we can create a fragmentation theory based on examination of sets of known structures and their associated mass spectra. All of these operations can, in principle be translated into a set of instructions in a computer program. Some of these programs we have presented in the past. One of these programs uses a combination of a predictor which uses a theory of mass spectrometry to predict the spectra of candidate structures, and an evaluation function which compares the predictions with the observed spectrum of the unknown, assigning a goodnes-of-fit score to each candidate.

Interpretation expected by the referring The technical issues of acquiring this knowledge, physician.

Each computing facility, which we will call a node, includes a process which is in charge of the management of some section of the data base. Each node is connected to the other nodes to allow the sharing of data. We will assume that there is neither shared memory nor global data in the system. Except for this restriction, our model will not specify the interconnection architecture of the network. Au important objective of our studies is in fact the development of measures of computational activities which will lead to evaluation criteria for architectural alternatives [10].

Report 77-20 Computer Assisted Structure Elucidation Using Stanford KSL Automatically Acquired 13C NMR Rules. Computer-Assisted Structure Elucidation Using Automatically Acquired '3C NMR Rules Carbon-13 nuclear magnetic resonance (CMR) has developed into an important tool for the structural chemist. A CMR spectrum exhibits a wide range of shifts which have been shown to have a strong correlation with structure(1 2). A natural abundance CMR spectrum which is fully proton decoupled consists of a number of sharp peaks which correspond to the resonance frequencies in an applied magnetic field of the various types of carbon atoms present. A C-13 shift is the amount an observed peak is shifted from that of a reference peak, usually tetramethylsilane (TMS). Molecular structure elucidation using CMR consists of establishing a set of rules which summarize the CMR behavior for a set of compounds and then using the rules to identify unknown compounds. In the traditional approach to structure elucidation using CMR the chemist forms a set of empirical rules by sorting through a large amount of data looking for correlations between structural arrangements in the molecuies and the observed C-13 shift. The total shift is then given as a function of these structural parameters. The functional fort, is usually chosen to be a linear combination of independent parameters. The optimized value of the coefficient of each structural parameter is obtained by a curve fitting procedure.

This characterization of the domain transformations leads naturally to the use of abstract objects and transformations in planning experiments. The abstractions correspond to the conceptual entities and transformations of the geneticist and will be an important part of the knowledge base.

The Experimental Steps in Detaii... 5 IV. Review of Knowledge Used in This Experiment 10 IV51 Proposing the Experiment..... 10 IV.2 Establishing Some Subgoals.

The DENDRAL research project was started in 1965 by Professors J. Lederberg and E.A. Feigenbaum and now includes Professor C. Djerassi in the Chemistry Department and about 20 other persons in Computer Science, Chemistry and Genetics. There are several aspects to the whole project, including research in chemistry and genetics, development of new chemical instrumentation and supporting computer programs, as well as artificial intelligence research. We have had two main computer science goals in this work: to study scientific inference and to aid working scientists. The two programs described below illustrate these concerns. Heuristic DENDRAL The Heuristic DENDRAL Program is designed to aid organic chemists determine the molecular structure of unknown compounds. Parts of the program have been highly tuned to work with experimental data from an analytical instrument known as a mass spectrometer. Mass spectrometry is a new and still developing analytical technique. It is not ordinarily the only analytic technique used by chemists, but is one of a broad array of analytic techniques including NMR, IR, UV, and "wet chemistry" analysis. It is particularly useful when the quantity of the sample to be identified is very small, for mass spectrometry requires only micrograms of sample.