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

COMPUTER PROGRAM APPLIED TO INFECTIOUS DISEASES* Edward H. Shortliffe Cepartmenc of!'edicine Stanford University School of Medicine Stanford, California 94305 A rule-based expert system is described which uees artificial intellieence techniques, and a model of:he iateractica between phesiciane and human consul-:ants, to attempt to satisfy the demands of a user:o unIr7 that is often reluctant to experiment with touter zecnnology. Experteace to date has demonstrated that the program is efficient, relacively easy to use, and reliable in the domain ofbacearemei therapy selection. Future work will involve broadening dad evaluatim; tne program's expertise in other areas of infectaoue disease therapy. Ihtreductioa Few eotentialusereopulations are as demanding of tomeuter tecenology as are practicing physicians. This our to a variety of factors which include the,eysecian's independeace as a lone decision maker, the seriousaess wieh which he views actions that may often have life-and-Ceach sigaifizance, and the overwhelming:t.me

A rule-based expert system is described which uses artificial intelligence techniques, and a model of the Interaction between physicians and human consultants, to attempt to satisfy the demands of a user community that Is often reluctant to experiment with computer technology. Experience to date has demonstrated that the program Is efficient, relatively easy to use, and reliable in the domain of bacteremia therapy selection. Future work will involve broadening and evaluating the program's expertise In other areas of infectious disease thPrapy. To that end rules regarding diagnosis and treatment of meningitis have been written and are currently under evaluation.

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.

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

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.

A range of different encoding techniques have been developed, along with a number of approaches to applying knowledge. Most of the effort to daze however, has concentrated on representing and manipulating knowledge about a specific domain of application, like game-playing ([14D, natural language understanding ([153, [19]), speech understanding ([8], [II)), chemistry ([7]), etc. This paper explores a number of issues involving representation and use of what we term meta-level knowledge, or knowledge about knowledge'. It begins by defining the term, then exploring a few of its varieties and considering the range of capabilities it makes possible. Four specific examples of meta-level knowledge are described, and a demonstration given of their application to a number of problems, including interactive tranfer of expertise and the "intelligent" use of knowledge. Finally, we consider the long term implications of the concept and its likely impact on the design of large programs.