System Overview 101 A second constraint was the need to design the program to accommodate a large and changing body o.f technical knowledge. It has become clear that large amounts of task-specific knowledge are required for high performance and that this knowledge base is subject to significant changes over time (Buchanan and Lederberg, 1971; Green et al., 1974). Our choice of a production rule representation was significantly influenced by such features of the knowledge base. A third demand was for a system capable of handling an interactive dialogue and one that was not a "black box." This meant that it had to be capable of supplying coherent explanations of its results, rather than simply pririting a collection of orders to the user.

By providing an environment for encoding knowledge, editing the evolving knowledge base, and testing programs, these systems provide techniques and tools that promise to be very versatile in helping to design new medical expert systems. While the earlier chapters in this volume provide motivation for applying artificial intelligence techniques to medicine, comparing the methods to those of traditional algorithmic programming and statistics, in this paper Kulikowski presents the knowledge-based perspective as a whole. This serves as a prelude to detailed discussions of particular consultation systems (Chapters 5, 6, 7, and 8) and to Szolovits and Pauker's analysis of medical reasoning in the context of these programs (Chapter 9).

The ultimate systems art' still probably many decades away, but existing techniques help define a subset of problems with which we are alread,'V prepared to deal.

The resulting instrument produces interpretations as well as the usual protein electrophoresis curves and component percentages. It is a commercially available productthe first marketed medical device to have used AI techniques in its development.

Specialization and generalization are accomplished by adding or deleting elements in these lists. The use of symbolic categories of belief (definite, probable, and possible) provides a specifiable means for manipulating the rules. While based on a simple idea, the SEEK program convincingly demonstrates the value of a rich('v structured representation and of reasoning from cases as a way of constructing a model. That is, exjJert knowledge is inseparable from case experience (Schank, 1983), in so far as knov.Jledge explains the cases. The use of a knowledge base to provide an explanatm), model has characterized other recent AIM work as well (cf.

Working in the EMYCIN environment, they were delighted to find that they could more easily distill their criteria for test interpretation by using the production rule formalism.

Every year, as computers become more powerful and less expensive, increasing amounts of health care data are recorded on them. Motivation for collecting data routinely into ambulatory and hospital medical record systems comes from all quarters. Health practitioners require sets of data for clinical management of individual patients. Hospital administrators require them for billing and resource allocation.

Examining the refinement structure created by the automatic programmer makes possible justifications of the code. This chapter describes XP LAIN and outlines additional advantages this approach has for explanation. The significance of Swartout's work is not just its use of a s_vstem design technique that makes explanation possible. His work reveals how principles (here, domain strategies by which specific treatment methods are apphed) are part of explanation. It is useful to supply not just an "audit trail" of what a problem solver did (on perhaps

The work is also of interest because of its relation to psychological studies (Chapter 12) and explanation methodology (Chapter 16).

Nor are they claiming that experts do not use general principles for ordering search and selecting alternatives (cf.