Artificial intelligence, or AI, is largely an experimental science—at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments.

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Editors' Note: Many expert systems require some means criticisms of this approach from those steeped in the practical of handling heuristic rules whose conclusions are less than certain issues of constructing large rule-based expert systems. Abstract the expert system draws inferences in solving different problems. Doyle's paper argues that it is difficult for a human expert "certainty factors," and in spite of the experimentally observed insensitivity of system performance to perturbations of the chosen values Recent successes of "expert systems" stem from much Research Projects Agency (DOD), ARPA Order No. 3597, monitored In the following, we explain the modified approach together with its practical and theoretical attractions. The client's income bracket is 50%, can be found (Minsky, 1975; Shortliffe & Buchanan, 1975; and 2. The client carefully studies market trends, Duda, Hart, & Nilsson, 1976; Szolovits, 1978; Szolovits & THEN: 3. There is evidence (0.8) that the investment Pauker, 1978). Reasoned Assumptions (from Davis, 1979) and would use the rule to draw conclusions whose "certainty factors" depend on the observed certainty Although our approach usually approximates that of Bayesian probabilities, accommodates representational systems based on "frames" namely as subjective degrees of belief.

REVEREND BAYES ON INFERENCE ENGINES: A DISTRIBUTED HIERARCHICAL APPROACH(*)(**) Judea Pearl Cognitive Systems Laboratory School of Engineering and Applied Science University of California, Los Angeles 90024 ABSTRACT This paper presents generalizations of Bayes likelihood-ratio updating rule which facilitate an asynchronous propagation of the impacts of new beliefs and/or new evidence in hierarchically organized inference structures with multi-hypotheses variables. The computational scheme proposed specifies a set of belief parameters, communication messages and updating rules which guarantee that the diffusion of updated beliefs is accomplished in a single pass and complies with the tenets of Bayes calculus. Introduction This paper addresses the issue ofefficiently propagating the impact of new evidence and beliefs through a complex network of hierarchically organized inference rules. Such networks find wide applications in expert-systems Cl], [2],[3],speech recognition [4], situation assessment [5], the modelling of reading comprehension [6] and judicial reasoning [7]. Many AI researchers have accepted the myth that a respectable computational model of inexact reasoning must distort, modify or ignore at least some principles of probability calculus.

Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof.

How do practicing physicians make clinical decisions? What techniques can we use in the computer to produce programs that exhibit medical expertise?

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In the spring of 1971, I attended a course on statistical inference taught by Arthur Dempster at Harvard. In the fall of that same year Geoffrey Watson suggested I give a talk expositing Dempster's work on upper and lower probabilities to the Department of Statistics at Princeton. This essay is one of the results of the ensuing effort. It offers a reinterpretation of Dempster's work, a reinterpretation that identifies his "lower probabilities" as epistemic probabilities or degrees of belief, takes the rule for combining such degrees of belief as fundamental, and abandons the idea that they arise as lower bounds over classes of Bayesian probabilities.

Lexical ambiguity can be syntactic if it involves more than one grammatical category for a single word, or semantic if more than one meaning can be associated with a word. In this article we discuss the application of a Bayesian-network model in the resolution of lexical ambiguities of both types. The network we propose comprises a parsing subnetwork, which can be constructed automatically for any context-free grammar, and a subnetwork for semantic analysis, which, in the spirit of Fillmore's (1968) case grammars, seeks to fulfill the required cases of all candidates for verb of the sentence. Solving for the highest joint probability of the variables conditioned upon the evidences to the network yields the most likely candidate with its meaning, along with its cases and respective meanings. This is achieved by fixing the values of all evidence nodes concurrently, and then performing a stochastic simulation in which the remaining nodes are updated probabilistically with a high degree of parallelism.

This text is a description of a computer-based system designed to assist physicians with clinical decision-making. This system, termed MYCIN, utilizes computer techniques derived principally from the subfield of computer science known as artificial intelligence (AI). MYCIN's task is to assist with the decisions involved in the selection of appropriate therapy for patients with infections.

MYCIN contains considerable medical expertise and is also a novel application of computing technology. Thus, this text is addressed both to members of the medical community, who may have limited computer science backgrounds, and to computer scientists with limited knowledge of medical computing and clinical medicine. Some sections of the text may be of greater interest to one community than to the other. A guide to the text follows so that you may select those portions most pertinent to your particular interests and background.

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Reprinted in Readings in Uncertain Reasoning, G. Shafer and J. Pearl, eds., pp. 259-273, San Mateo, CA: Morgan Kaufmann Publishers, Inc., 1990.See also: Stanford Center for Biomedical Informatics Research (BMIR).… quantifying confirmation and then manipulating the numbers as though they were probabilities quickly leads to apparent inconsistencies or paradoxes. Carl Hempel presented an early analysis of confirmation (Hempel, 1965), pointing out as we have that C[h,e] is a very different concept from P(hle ). His famous Paradox of the Ravens was presented early in his discussion of the logic of confirmation. Let hl be the statement that "all ravens are black" and h2 the statement that "all nonblack things are nonravens." Clearly hi is logically equivalent to h,2. If one were to draw an analogy with conditional probability, it might at first seem valid, therefore, to assert that C[hl,e] = C[h2,e] for all e. However, it appears counterintuitive to state that the observation of a green vase supports hi, even though the observation does seem to support h,2. C[h,e] is therefore different from P(hle) for it seems somehow wrong that an observation of a vase could logically support an assertion about ravens. Another characteristic of a quantitative approach to confirmation that distinguishes the concept from probability was well-recognized by Carnap (1950) and discussed by Barker (1957) and Harrd (1970). They note it is counterintuitive to suggest that the confirmation of the negation of a hypothesis is equal to one minus the confirmation of the hypothesis, i.e., C[h,e] is not 1 - C[-qh,e]. The streptococcal decision rule asserted that a gram-positive coccus growing in chains is a Streptococcus with a measure of support specified as 7 out of 10. This translates to C[h,e]=0.7 where h is "the organism is a Streptococcus" and e is the information that "the organism is a gram-positive coccus growing in chains." As discussed above, an expert does not necessarily believe that C[mh,e] = 0.3. The evidence is said to be supportive of the contention that the organism is a Streptococcus and can therefore hardly also support the contention that the organism is not a Streptococcus. Ch.13 of Mycin Book; revised from Math. Biosci. 23:351-379