Steps toward a theoretic foundation for complex knowledge-based CAI.

Rephrases the question and gives examples of recognized responses.

In this book we have presented experimental evidence at many levels of detail for a diverse set of hypotheses. As indicated by the chapter and section headings, the major themes of the MYCIN work have many variations. In this final chapter we will try to summarize the most important results of the work presented. This recapitulation of the lessons learned should not be taken as a substitute for details in the sections themselves. We provide here an abstraction of the details, but hope it also constitutes a useful set of lessons on which others can build. The three main sections of this chapter will reiterate the main goals that provide the context for the experimental work; discuss the experimental results from each of the major parts of the book; and summarize the key questions we have been asked, or have asked ourselves, about the lessons we have learned. If we were to try to summarize in one word why MYCIN works as well as it does, that word would be flexibility.

This chapter describes an oncology protocol management system, named ONCOCIN after its domain of expertise (cancer therapy) and its historical debt to MYCIN. The program is actually a set of interrelated subsystems, the 1 primary ones being: 1. the Reasoner, a rule-based expert consultant that is the core of the system; and 2. the Interviewer, an interface program that controls a high-speed terminal and the interaction with the physicians using the system. The Interviewer is described in some detail in Chapter 32. This chapter describes the problem domain and the representation and control techniques used by the Reasoner. This chapter is based on an article originally appearing in Proceedings of the Seventh IJCAI, 1981, pp. Used by permission of" International Joint Conferences on Artificial Intelligence, Inc.; copies of the Proceedings are available from William Kaufmann, Inc., 95 First Street, l.os Ahos, CA 94022. Another program, the Intemctor, handles interprocess communication.

A number of computer programs have been developed to assist physicians with diagnostic or treatment decisions, and many of them are potentially very useful tools. However, few systems have undergone evaluation by independent experts. The task evaluated was the selection of antimicrobials for cases of acute infectious meningitis before the causative agent was identified. MYCIN was originally developed in the domain of bacteremias and then expanded to include meningitis. Its task is a complicated one; it must decide whether and how to treat a patient, often in the absence of microbiological evidence. It must allow for the possibility that any important piece of information might be unknown or uncertain.

Here we consider the logical bases for rules: what kinds of arguments justify the rules, and what is their relation to a mechanistic model of the domain? We use the terms "explain" and "justify" synonymously, although the sense of "making clear what is not understood" (explain) is intended more than "vindicating, showing to be right or lawful" (justify).

This chapter explores a number of issues involving representation and use of what we term meta-level knowledge, or knowledge about knowledge.1 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 transfer 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. The context of this work is the TEIRESIAS program discussed in Chapter 9. In the earlier chapter we focused on the use of TEIRESIAS for knowledge acquisition. Here we focus on the classification and types of knowledge used by TEIRESIAS. In the most general terms, meta-level knowledge is knowledge about knowledge. Its primary use here is to enable a program to "know what it knows," and to make multiple uses of its knowledge. As mentioned in Chapter 9, the program is not only able to use its knowledge directly, but may also be able to examine it, abstract it, reason about it, or direct its application. This chapter discusses examples of meta-level knowledge classified along two dimensions: (i) specificity character (representation-specific vs. domain-specific), and (ii) source (user-supplied vs. derived). Representation-specific meta-level knowledge involves supplying a program with a store of knowledge dealing with the form of its representations, in particular, their design and organization. Traditionally, this design and organization infor-This chapter is an expanded and edited version of a paper originally appearing in Proceedings of the Fifth IJCAL 1977, pp. Used by permission of International Joint Conferences on Artificial Intelligence, Inc.; copies of the Proceedings are available from William Kaufmann, Inc., 95 First Street, Los Altos, CA 94022. IFollowing standard usage, knowledge about objects and relations in a particular domain will be referred to as object-level knowledge. Type declarations are a small step toward more explicit specification of this information, especially as they are used in extended data types and record structures. As we discuss below, this sort of information, along with a range of other facts about representation design, can be employed quite usefully if it is made explicit and made available to the system.

Performance Level D-RULE578 IF: 1) The infection which requires therapy is meningitis, and 2) Organisms were not seen on the stain of the culture, and 3) The type of the infection is bacterial, and 4) The patient has been seriously burned THEN: There is suggestiv evidence (.5) that pseudomonas-aeruginosa is one of the organisms (other than those seen on cultures or smears) which might be causing the infection UPDATES: COVERFOR USES: (TREATINF ORGSEEN TYPE BURNED) Support Level MECHANISM-FRAME: BODY-INFRACTION.WOUNDS JUSTIFICATION: "For a very brief period of time after a severe burn the surface of the wound is sterile. Shortly thereafter, the area becomes colonized by a mixed flora in which gram-positive organisms predominate. By the 3rd post-burn day this bacterial population becomes dominated by gram-negative organisms. By the 5th day these organisms have invaded tissue well beneath the surface of the burn. The organisms most commonly isolated from burn patients are Pseudomonas, Klebsiella-Enterobacter, Staph., etc. Infection with Pseudomonas is frequently fatal." LITERATURE: MacMillan BG: Ecology of Bacteria Colonizing the Burned Patient Given Topical and System Gentamicin Therapy: a five-year study, J Infect Dis 124:278-286, 1971.

We have so far described MYCIN largely in terms of its knowledge base and inference mechanism, and specifically in terms of rules and a rule interpreter that allow high-performance problem solving. In Chapters 27 through 29 we describe additional knowledge structures that increase the flexibility and transparency of' MYCIN's knowledge base. We refer to many of these as meta-level knowledge. When we speak of meta-level knowledge we mean nothing more than knowledge about knowledge. In a computer program it needs to be represented and interpreted in order to be useful, but the main idea is that it can be an explicit, and flexible, element of expertise. For example, metalevel knowledge can help in modifying an existing rule and in integrating the modification into the whole rule set because it provides additional information about the existing rules to the editor. The ideas for using meta-level knowledge in MYCIN grew out of several projects that Randy Davis was working on in the mid-1970s.

CENTAUR was designed in response to problems that occurred while using a purely rule-based system. The CENTAUR system offers an appropriate environment in which to experiment with knowledge representation issues such as determining what knowledge is most easily represented in rules and what is most easily represented in frames. In summary, much research remains to be done on this and associated knowledge representation issues. This present research is one attempt to make explicit the art of choosing the knowledge representation in AI by drawing comparisons between various approaches and by identifying the reasons for selecting one fundamental approach over another.