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.

ABSTReCT The research activities of the Heuristic Programming Project, for the four-year period ending July 31, 1977, are summarized in this report. Contributions to Knowledge Engineering research in the fields of knowledge acquisition (both interactive and automated), knowledge representation and knowledge utilization were reported in over thirty publications by members of the project. A summary of those publications is?resented here. The Al Handbook, an encyclopedic reference to the field of::tificial Intelligence, is described in the appendix, along with the excecteç table of contents and sample articles.

In the early days of computing, these goals were central to the new discipline called cybernetics [126], [2]. Over the past two decades, progress toward these goals has come from a variety of fields - notably computer science, psychology, adaptive control theory, pattern recognition, and philosophy. Substantial progress has been made in developing techniques for machine learning in highly restricted environments.

Over tt7e years a range of different mechanisms have been proposed and used (e.g., standard procedure invocation, goal-directed invocation, etc.), each typically motivated by the attempt to develop new forms of knowledge encoding (e.g., procedures, PLANNER theorems, etc.). We consider in this paper tne strengths and weaknesses of a range of these mechanisms, paying particular attention to their expressiveness and validity. This analysis brings to light certain shortcomings shared to some degree by all current mechanisms. A number of ideas are presented as the basis for a mechanism which appears to offer a way of overcoming the problems discovered. We describe how those ideas have been implemented and tested in a rule-based system, and explore their impact on system performance, ease of construction, and flexibility. We consider also their value as a generalization of the existing notions of procedure calling. Though the terminology may differ, some of the shortcomings we point out and some of the ideas proposed may be recognized by others who have built similar systems, where some of these ideas have been Implemented in various Informal ways. The purpose of this paper is not, therefore, to advocate a particular solution, but instez.d