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KNOWLEDGE ENGINEERING the Applied Side of Artificial Intelligence

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KNOWLEDGE ENGINEERING the Applied Side of Artificial Intelligence Stanford Heuristics Programming Project September 1980 Report No. HPP-80-21 Department of Computer Science Report No. STAN-CS-80-812 KNOWLEDGE ENGINEERING The Applied Side of Artificial Intelligence by Edward A. Feigenbaum Research sponsoredby Office of Naval Research DEPARTMENT OFCOMPUTER SCIENCE Stanford University Knowledge Engineering: The Applied Side of Artificial Intelligence Edward A. Feigenbaum Computer Science Department Stanford University * Stanford, California 94305 Copyright (c) l9Bo Edward A. Feigenbaum ALL RIGHTS RESERVED ■: -» «. Knowledge Engineering: The Applied Side of Artificial Intelligence Edward A. Feigenbaum Computer Science Department Stanford University Stanford, CA USA 94305 1.0 Introduction: Symbolic Computation and Inference This paper will discuss the applied artificial intelligence work that is sometimes called "knowledge engineering". The work is based on computer programs that do symbolic manipulations and symbolic inference, not calculation. The programs I will discuss do essentially no numerical qualitative calculation. They discover lines-of-reasoning leading to solutions to problems stated symbolically. 1.1 Knowledge Since in this paper I often use the term "knowledge", let me say what I mean by it. The knowledge of an area of expertise—of a field of practice—is generally of two types: a) Facts of the domain—the widely shared knowledge that is written in textbooks, and in journals of a field; that constitutes the kind of professor material that a would lecture about in a class, b) Equally as important to the practice of a field is the heuristic knowledge—knowledge which constitutes the rules of expertise, the rules of good practice, the judgmental rules of the field, the rules of plausible reasoning. These rules collectively constitute what the mathematician, George Polya, has called the "art of good guessing". In contrast to the facts of the field, its rules of expertise, its rules of good guessing, are rarely written down. This knowledge is transmitted in internships, Ph.D. programs, apprenticeships. The programs I will describe require, for expert performance on problems, heuristic knowledge to be combined with the facts of the discipline. 1.2 Expert Systems The act of obtaining, formalizing and putting to work these kinds of rules is what we call "expertise modeling". In the modeling of expertise, we construct programs called "expert systems". The goal of an "expert system" project is to write a program that achieves a high level of performance on problems that are difficult enough to require significant human expertise for their solution. The more common strategy of AI research is to choose a highly simplified problem— sometimes called, a "toy problem"—and exploit the toy problem in depth. In the problems we contrast, choose require the expertise of an M.D., or a Ph.D., or at least a very highly trained, specialist in a field, to solve. An expert system of this type consists of only two things: a base, an knowledge and inference procedure. The knowledge base contains the facts and heuristics; the * inference procedure consists of the processes that work over the knowledge base infer to solutions to problems, to do analyses, to form hypotheses, etc. In principle, the knowledge base is separable from the inference procedure. 1.3 The Scientific Issues Underlying Knowledge Engineering What are the central scientific issues of the Artificial Intelligence field from which this more applied research draws its inspiration? I'd like to categorize these under three headings. field* repreL^S «** the of the *?. **»*«*>«S can be-conveniently acces^ for probl^sol^ngT^ C<>m^tet' ° they inference engine. L f ei^TS ULSJ** ° poss^e't^lc^e^^iege L^rtant foVf^sW5^ *?" IS of^fS^^^ -Mr-a^sra^ f intelligence. For a long^ rtificial ° was cloaked uX^^rd^Sf,the word "learning".? able to be more precise ahnnh ?h» ZtltS , Now£ we are increased P-isionKme^w^ -* this . diligence research" £ SSJW^J* *£ X 06 knowledge is powerf The of «'= Programs, power «S5 does^not^f^ f renC Procedure resides in the specifl^Se of orohl l »" systems Sf "V" " - we will ta building will I^%A^^^J?ZSSS? of means for mUSt haVe more al*omatic «■ replacing what^s currenUv o^'^ procedure. The preblm time-consuming araJ expensive of knowledgeWled9e ««*»»tionacoulsitiofis the critical in Artificial Intelligence "° bottleneck problem 2.0 A Brief Tutorial Using the MVCIN Program As the basis of the exposition , of underlvina ideas T win , . *.«»uj.l* exogenous possibly infer). \ data the computer couldn't reaL^aS tfa^resul t"S tSKET dladSSff9nosis) \We can expect that it should be ableWIB touo exolainexplain " ' "^ tnathhati^!!lme-of-reasomna- to thp near t« p^~+. % v t " 2.1 Knowledge in MYCIN rod ctlon rule consists of an "if" part and a (sometimes calledTV !UC^ .P V " "then" part situation part" and "action part"). The "if part" defines a set of „=^ conditions of relevancy such that if each of these clauses is true, then the conclusion follows. (2) The rule is shown in approximately the way the expert would enter it; and exactly the way the doctor would see the rule if it were displayed This piece of knowledge will be evoked from the knowledge base if the conditions are true, and will be built into the line-of-reasoning. If: 1) The infection which requires therapy is meningitis, and 2) The type of the infection is fungal, and 3) Organisms were not seen on the stain of the culture, and 4) The patient is not a compromised host, and 5) The patient has been to an area that is endemic for coccidiomycoses, and 6) The race of the patient is one of: black asian indian, and 7) The cryptococcal antigen in the csf was not positive Then: there is suggestive evidence that cryptococcus is not one of the organisms which might be causing the infection. Figure 1: A piece of knowledge in MYCIN 2.2 Inexact Inference In MYCIN, there is a way for the expert to state to what extent, on a scale from 0.1 to 1.0, he believes that the preconditions imply the conclusion. 1.0 is definitional certainty; 0.9 is "very strong evidence"; 0.6 is "suggestive evidence"; and so on. These r indices are combined in MYCIN with a very- simple and easily function, explained yielding an index called a "cumulative certainty factor", an index of "strength of belief" in the line-of-reasoning. 2.3 MYCIN Diagnosis and Therapy Figure 2 shows a typical MYCIN diagnosis. Then MYCIN proceeds to conduct a shorter consultation with the physician about such things as the patient's known sensitivities to various types of antibiotics and the resistance shown by certain organisms to various kinds of antibiotics. MYCIN then produces a therapy recommendation such as shown in Figure 3. INFECTION-1 is MENINGITIS + <ITEM-1> E.COLI [from clinical evidence only] + <ITEM-2> PSEUDCMCNAS-AERLCINOSA [from clinical evidence only] + <ITEM^-3> KLEBSIELLA-PNEUMONIAE [from clinical evidence only] + <ITEM-4> DIPLCCOCCUS-PNEUMCNIAE [from clinical evidence only] Figure 2: An example of a MYCIN diagnosis 4 My preferred therapy recommendation is as follows: In order to cover for Items <1 2 3 4>: Give the following in combination: 1) AMPICILLIN Dose: 3.5g (28.0 ml) q4h IV [calculated on basis of 50 mg/kg] 2) GENTAMICIN Dose: 11 ing (3.0 ml, 80mg/2ml ampule) qSh IV [calculated on basis of 1.7 mg/kg] plus consider giving 5 mq q24h Intrathecal Comments: Monitor serum concentrations Since high concentrations of penicillins can inactivate aminoglycosides, do not mix these two antibiotics in the XV Dottle. same Figure 3: An example of a MYCIN antibiotic therapy recommendation 2.4 MYCIN'S Line-of-Reasoning MYCIN'S line-of-reasoning a is chain of rules that concludes the (oerhans uncertain) presence of an infecting organism from laboratory test data and W history. The line-of-reasoning is St discovered by backward cZUT. tt search^tarts Vari US P SSible rganianS aS "g aIS t0 be 5?data ° ° ° ° «*i^i uSSbTSS The explanation facility can exhibit selected portions of the requested by the user. It can chain as answer a variety of queries during or after the dialogue, such as: "Why (are you asking me for this information)?" consultation particular or "How some conclusion reached) ?"Che interesting form of query is shown in FwfT To answerit MYCIN must keep track of not only also the 9 invalid lines explored, along with reasonsacxre^^ll^Sr^S"^for unacceptability ' USER: WHY DIDN'T YOU GIVE TETRACYCLINE FOR E.COLI IN REC-1 MYCIN: TETRACYCLINE was discounted for ITEM-1 (RECQvIMENDATION-1 ) because there is evidence that this e.coli is not sensitive to it. Figure 4: An example of MYCIN'S explanation facility 2.5 MYCIN'S Inference Procedure We can remove the knowledge base of MYCIN and substitute a set of rules uivalent from ise^ *> saying that the knowledge base and the ITfTrtl.T^ rt SyStem ts cMIe ' «*£ infLtioSslisLse'dl a ddiSSEa<3nosls rules yields'VS an inference "engine"R^^ EMYCINYr (for,r^°r?c f? f MI * which we call " Essential Mycin or Empty Mycin or Engine Mycin). 3.0 Building a New System with the EMYCIN Tool: PUFF Combining with EMYCIN a set of rules for pulmonary function diagnosis (diagnosis eS) rod Ced a ?M^?, di^ostic program called PUFF (Osborn et al. In this diagnosticdlSe^ situation,-F " a patient 1979?? is breathing in and out of an instrument cailS a spirometer, producing measurements of flow of air in expiration and inhalation versus lung volume. Data reduction is done by a PDP-11, and data interpretation done by is PUFF. (3) The PUFF report is reviewed by an expert physician, is signed accurate, if put into the patient record and sent to the referring physician Currently, per about 85 cent of the PUFF outputs are signed without modification.
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