Randall Davis

Diagnostic reasoning based on structure and behavior

Abstract We describe a system that reasons from first principles, i.e., using knowledge of structure and behavior. The system has been implemented and tested on several examples in the domain of troubleshooting digital electronic circuits. We give an example of the system in operation, illustrating that this approach provides several advantages, including a significant degree of device independence, the ability to constrain the hypotheses it considers at the outset, yet deal with a progressively wider range of problems, and the ability to deal with situations that are novel in the sense that their outward manifestations may not have been encountered previously. As background we review our basic approach to describing structure and behavior, then explore some of the technologies used previously in troubleshooting. Difficulties encountered there lead us to a number of new contributions, four of which make up the central focus of this paper. • — We describe a technique we call constraint suspension that provides a powerful tool for troubleshooting. • — We point out the importance of making explicit the assumptions underlying reasoning and describe a technique that helps enumerate assumptions methodically. • — The result is an overall strategy for troubleshooting based on the progressive relaxation of underlying assumptions. The system can focus its efforts initially, yet will methodically expand its focus to include a broad range of faults. • — Finally, abstracting from our examples, we find that the concept of adjacency proves to be useful in understanding why some faults are especially difficult to diagnose and why multiple representations are useful.

What Is a Knowledge Representation

Although knowledge representation is one of the central and, in some ways, most familiar concepts in AI, the most fundamental question about it -- What is it? -- has rarely been answered directly. Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, and still others have focused on properties that are important to the notion of representation in general. In this article, we go back to basics to address the question directly. We believe that the answer can best be understood in terms of five important and distinctly different roles that a representation plays, each of which places different and, at times, conflicting demands on the properties a representation should have. We argue that keeping in mind all five of these roles provides a usefully broad perspective that sheds light on some longstanding disputes and can invigorate both research and practice in the field.

Production rules as a representation for a knowledge-based consultation program

The MYCIN system has begun to exhibit a high level of performance as a consultant on the difficult task of selecting antibiotic therapy for bacteremia. This report discusses issues of representation and design for the system. We describe the basic task and document the constraints involved in the use of a program as a consultant. The control structure and knowledge representation of the system are examined in this light, and special attention is given to the impact of production rules as a representation. The extent of the domain independence of the approach is also examined.

Interactive transfer of expertise: Acquisition of new inference rules

Abstract teiresias is a program designed to provide assistance on the task of building knowledge-based systems. It facilitates the interactive transfer of knowledge from a human expert to the system, in a high level dialog conducted in a restricted subset of natural language. This paper explores an example of teiresias in operation and demonstrates how it guides the acquisition of new inference rules. The concept of meta-level knowledge is described and illustrations given of its utility in knowledge acquisition and its contribution to the more general issues of creating an intelligent program.

Computer-based consultations in clinical therapeutics: Explanation and rule acquisition capabilities of the MYCIN system☆

Abstract This report describes progress in the development of an interactive computer program, termed MYCIN, that uses the clinical decision criteria of experts to advise physicans who request advice regarding selection of appropriate antimicrobial therapy for hospital patients with bacterial infections. Since patients with infectious diseases often require therapy before complete information about the organism becomes available, infectious disease experts have identified clinical and historical criteria that aid in the early selection of antimicrobial therapy. MYCIN gives advice in this area by means of three subprograms: (1) A Consultation System that uses information provided by the physician, together with its own knowledge base, to choose an appropriate drug or combination of drugs; (2) An Explanation System that understands simple English questions and answers them in order to justify its decisions or instruct the user; and (3) A Rule Acquisition System that acquires decision criteria during interactions with an expert and codes them for use during future consultation sessions. A variety of human engineering capabilities have been included to heighten the programs acceptability to the physicians who will use it. Early experience indicates that a sample knowledge base of 200 decision criteria can be used by MYCIN to give appropriate advice for many patients with bacteremia. The system will be made available for evaluation in the clinical setting after its reliability has been shown to approach that of infectious disease experts.

Qualitative rigid-body mechanics

We present a theory of qualitative rigid body mechanics and describe a program that uses this theory to compute qualitative dynamic simulations. The program works directly from a qualitative representation of geometry (qc-space). It employs a new qualitative representation for forces that reduces ambiguity in force sums and hence reduces branching.

Generalized procedure calling and content-directed invocation

We suggest that the concept of a strategy can profitably be viewed as knowledge about how to select from among a set of plausibly useful knowledge sources , and explore the framework for knowledge organization which this implies. We describe meta rules , a means of encoding strategies that has been implemented in a program called TEIRESIAS, and explore their utility and contribution to problem solving performance. Meta rules are also considered in the broader context of a tool for programming. We show that they can be considered a medium for expressing the criteria for retrieval of knowledge sources in a program, and hence can be used to define control regimes. The utility of this as a programming mechanism is considered. Finally, we describe the technique of content-directed invocation used by meta rules, and consider its use as a way of implementing strategies. It is also considered in historical perspective as a knowledge source invocation technique, and its advantage over some existing mechanisms like goal-directed invocation is considered. This work was supported in part by the Bureau of Health Sciences Research and Evaluation of HEW under Grant HS-01544 and by the Advanced Research Projects Agency under ARPA Order 2494. It was carried out on the SUMEX Computer System, supported by the NIH under Grant RR-00785. The views expressed are solely those of the author.

Knowledge acquisition in rule-based systems: knowledge about representation as a basis for system construction and maintenance

Recent research efforts aimed at task-oriented systems have emphasized the importance of large stores of domainspecific knowledge as a basis for high performance. But assembling the required knowledge base is a difficult task that often extends over several years, and involves numerous modifications to the knowledge base. Given the difficulty of making even srnall changes to a program, this presents a challenging problem in system construction.

A DSS for diagnosis and therapy

This paper reviews an approach to the design and construction of a decision support system intended to function as a consultant on the question of diagnosis and therapy selection. It describes the system in terms of the nature of the decision problem involved, discusses factors which make the problem difficult and considers the design goals that have led to the construction of a system with several novel capabilities. Many of those capabilities result from representing domain-specific knowledge in the system in terms of numerous judgmental decision rules, and we examine a number of such rules. Examples of the system in operation are given to illustrate many of these issues, and performance is compared with previous approaches to automated medical decision making. Finally, we consider the domain independence and generality of the methodology and consider the potential impact the system may have as a tool for decision support.

KNOWLEDGE ACQUISITION IN RULE-BASED SYSTEMS—KNOWLEDGE ABOUT REPRESENTATIONS AS A BASIS FOR SYSTEM CONSTRUCTION AND MAINTENANCE1

Recent research efforts aimed at task-oriented systems have emphasized the importance of large stores of domain-specific knowledge as a basis for high performance. But assembling the required knowledge base is a difficult task that often extends over several years, and involves numerous modification to the knowledge base. Given the difficulty of making even small changes to a program, this presents a challenging problem in system construction. We have studied this issue in the context of TIERESIAS, a program designed to function as an assistant in the task of building large knowledge bases. TIERESIAS facilitates the interactive transfer of expertise from a human expert to the knowledge base of the system, in a dialog conducted in a restricted subset of natural language. One such knowledge transfer task involves teaching the system about a new conceptual primitive from which new inference rules can be built. We show that by providing a program with a store of knowledge about its own representations, this acquisition of new concepts can be carried out in a high-level dialog that transfers information efficiently. The necessary knowledge about representations includes both structural and organizational information, and is specified in a data structure schema, a device used to describe representations.