The editors have provided him with special introductory sections as a ing guide to a thorough understand of the reports.

Case-based reasoning is used extensively by people in A second driving force in the evolutionary history of CBR both expert and commonsense situations. It provides a was dissatisfaction with rule-based reasoning (expert systems wide range of advantages.

Step 6 is a goal-assertion the input, another algorithm might result. Thus one could resolution that functions similarly to the goal-goal resolution break a into a[1],..., a [length(a)/2] and a [length(a)/ above. The final synthesized program is: 2 1],..., a[length(a)] and find an algorithm that recursively calls f on both the first and second halves of its f(x) if x NIL then 0 else car(x) f(cdr(x)).

How do practicing physicians make clinical decisions? What techniques can we use in the computer to produce programs that exhibit medical expertise? Our interest in these questions is motivated by our desire: 1. to provide (by computer) expert medical consultation to general practitioners or paramedical personnel in communities where such consultation is normally unavailable; 2. to come to understand the reasoning processes of expert doctors so that we may improve the teaching of their skills to medical students; and 3. to advance the techniques of artificial intelligence, especially as applied to medicine (AIM), to support our other goals. In other publications, we have described research by our group on programs to take the history of the present illness of a patient with renal disease (Pauker and Gorry, 1976; Szolovits and Pauker, 1976) and to advise the physician in the administration of the drug digitalis to patients with heart disease (Gorry et al., 1978; Silverman, 1975; Swartout, 1977).

Turing's test (see evaluation) smart instruments, 345

Chapter 10 Chapter 11 Chapter 12 Using Rules The Evolution of MYCIN's Rule Form The Structure of the MYCIN System William van Melle Details of the Consultation System Edward H. Shortliffe Details of the Revised Therapy Algorithm WiUiam J. Clancey Building a Knowledge Base Knowledge Engineering Completeness and Consistency in a Rule-Based System Motoi Suwa, A. Carlisle Scott, and Edward H. Shortliffe Interactive Transfer of Expertise Randall Davis Reasoning Under Uncertainty Uncertainty and Evidential Support A Model of Inexact Reasoning in Medicine Edward H. Shortliffe and Bruce G. Buchanan Probabilistic Reasoning and Certainty Factors J. Barclay Adams 55 67 78 133 149 159 171 209 233 263

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

The drawbacks of pure probabilistic methods and of the certainty factor model have led us in recent years to consider alternate approaches. Particularly appealing is the mathematical theory of evidence developed by Arthur Dempster. We are convinced it merits careful study and interpretation in the context of expert systems. This theory was first set forth by Dempster in the 1960s and subsequently extended by Glenn Sharer. In 1976, the year after the first description of CF's appeared, Shafer published A Mathematical Theory of Evidence (Shafer, 1976). Its relevance to the issues addressed in the CF model was not immediately recognized, but recently researchers have begun to investigate applications of the theory to expert systems (Barnett, 1981; Friedman, 1981; Garvey et al., 1981). We believe that the advantage of the Dempster-Shafer theory over previous approaches is its ability to model the narrowing of the hypothesis set with the accumulation of evidence, a process that characterizes diagnostic reasoning in medicine and expert reasoning in general. An expert uses evidence that, instead of bearing on a single hypothesis in the original hypothesis set, often bears on a larger subset of this set. The functions and combining rule of the Dempster-Shafer theory are well suited to represent this type of evidence and its aggregation. For example, in the search for the identity of an infecting organism, a smear showing gram-negative organisms narrows the hypothesis set of all possible organisms to a proper subset. This subset can also be thought of as a new hypothesis: the organism is one of the gram-negative organisms.

The. development of automated assistance for medical diagnosis and decision making is an area of both theoretical and practical interest. Of methods for utilizing evidence to select diagnoses or decisions, probability theory has the firmest appeal. Probability theory in the form of Bayes' Theorem has been used by a number of" workers (Ross, 1972). Notable among recent developments are those of de Dombal and coworkers (de Dombal, 1973; de Dombal et al., 1974; 1975) and Pipberger and coworkers (Pipberger et al., 1975). The usefulness of Bayes' Theorem is limited practical difficulties, principally the lack of data adequate to estimate accurately the a priori and conditional probabilities used in the theorem. One attempt to mitigate this problem has been to assume statistical independence among various pieces of evidence. How seriously this approximation affects results is often unclear, and correction mechanisms have been explored (Ross, 1972; Norusis and Jacquez, 1975a; 1975b). Even the independence assumption requires an unmanageable number of estimates of" probabilities for most applications with realistic complexity.

Questioning of the expert gradually reveals, however, that despite the apparent similarity to a statement regarding a conditional probability, the number 0.7 differs significantly from a probability. The expert may well agree that P(hl]sl & s2 & s:0 0.7, but he becomes uneasy when he attempts to follow the logical conclusion that therefore P( hllS 1 & s 2 & s) 0.3. He claims that the three observations are evidence (to degree 0.7) in favor of the conclusion that the organism is a Streptococcus and should not be construed as evidence (to degree 0.3) against Streptococcus. We shall refer to this problem as Paradox 1 and return to it later in the exposition, after the interpretation of the 0.7 in the rule above has been introduced. It is tempting to conclude that the expert is irrational if he is unwilling to follow the implications of his probabilistic statements to their logical conclusions.