If no CONTROL slot is associated with a prototype, the Interpreter will attempt to fill in values for the prototype components in the order of their Importance measures. When all of the clauses in the CONTROL slot have been executed and the prototype has been instantiated, a decision is madel as to whether the prototype should be confirmed as matching the data in the case. The system then checks either the IF-CONFIRMED slot or the IF-DISPROVED slot to determine what should be done next. Similarly, the ACTION slot specifies stops to be taken for a confirmed prototype during the clean-up stage.

These links are based on domain facts and relations (e g. that a topic is the name of a block of Step?: Discuss the final hypothesis for the goal

EMYCIN is a programming system for writing knowledge-based consultation programs with a production-rule representation of knowledge. Several major components of the system, Including an explanation program and knowledge acquisition routines, are described. EMYCIN has been used to build consultation systems in several areas of medicine, as well as an engineering domain. These experiences lead to some general conclusions regarding the potential applicability of EMYCIN to new domains. Keywords: knowledge-based systems, production rules, knowledge representation, automated consultant.

AGE is a system designed to aid knowledge engineers build knowledge-based programs. We have built a laboratory of building blocks which the user can assemble in various ways to fit a particular problem. This paper describes the facilities available to the user and descibes an example program built with AGE.

A program for selecting antibiotic therapy is described that makes sophisticated recommendations and provides simple, useful explanations of its reasoning for a user. Our method is to structure the therapy optimization problem in terms of local and global solution criteria that are applied in a generate-and-test algorithm. Generation of therapy recommendations is directed by a fixed, ordered set of canonical instructions that describe the global characteristics of a recommendation. Other factors are dealt with in a "planning" phase particular to each organism for which therapy is to be prescribed, and the test phase that incorporates patient considerations such as allergies and age. We demonstrate the advantages of this canonical form for explanation, comparison of alternative recommendations, and a simple instructive capability.

We describe the development and (partial) Implementation of an "automated consultant" to advise non-expert engineers In the use of a general-purpose structural analysis program. The analysis program numerically simulates the behavior of a physical structure subjected to various mechanical loading conditions. The automated consultant, called SACON (Structural Analysis CONsultant), Is based on a version of the MYCIN program [Shortliffe74], originally developed to advise physicians In the diagnosis and treatment of infectious diseases. The domain-specific knowledge in MYCIN Is represented as situation-action rules, and is kept independent of the "inference engine" that uses the rules. By substituting structural engineering knowledge for the medical knowledge, the program was converted easily from the domain of Infectious diseases to the domain of structural analysis.

PUFF is now in routine use in Presbyterian Hospital, Pacific Medical Center (PMC), in San Francisco. The program produces a report, intended for patient records, that explains the clinical significance of measured quantitative test results and gives a diagnosis of the presence and severity of pulmonary disease in terms of the measured data, referral diagnosis, and patient history. "Rules", or statements of the form "IF condition THEN conclusion ", are used by the physiologist and the computer system to specify the system operation. The sequence of rules used to interpret the case also specifies a line of reasoning about the case, or the detailed explanation of the interpretation of the case. The use of rules for this type of knowledge based system is taken from the results of applied Artificial Intelligence research. In a 144 case prospective evaluation, there was a 91% overall rate of agreement between the rule based system diagnoses and the diagnoses of the designing physiologist; there was a 89% rate of agreement between the system diagnoses and diagnoses of a second independent physiologist.

References 67 October 27, 1977 1 Introduction This application addresses the continuation of research on the applications of artificial intelligence (Al) (1) to experimental molecular genetics. It is an extension of a longstanding effort to cultivate attention to ongoing laboratory research as a domain of explorations in artificial intelligence. Our major effort in this field had been in the DENDRNL project, with analytical organic chemistry as the object discipline.

This paper is a "final report" on the first version of the CRYSALIS project. As such, we will summarize the current state of the system and show where we plan to go with it. We have found that a design (in the software eng ineer ing sense) is a valuable tool for the evaluation and augmentation of a program, even when the design is done ex post facto. Using such a design, we discuss the major flaws of the existing system and how to correct them. Finally, we show how the architecture of this system could be useful for certain other task domains.

This is traditionally done with tne aid of a computer programmer acting as intermediary. The dire_t transfer of knowledge from an expert to the system requires a natural-language processor capable of handling a substantial subset of English. The development of such a natural-language processor is a long-term goal of automating knowledge acquisition; faciliting the interface between the expert and the system is a first step toward this goal. This paper describes BAUBAb, a program designed and implemented for hYCIN (Shortliffe 1974), a medical consultation system for infectious disease diagnosis and therapy selection. EAUdAb is concerned with the problem of parsing - recognizing natural language sentences aad encoding tnem into MICIN's internal representation. For this purpose, it uses a semantic grammar in whicft tne non-terminal symools denote semantic categories (e.g., infections and symptoms), or conceptual categories wnicn are common tools of knowledge representation in artificial intelligence (e.g.