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EXPERT SYSTEMS: AEPITCAT~ONS IN SPACE Bruce G. Buchanan ~TE~rXJOl'lON Artificial intelligent- is one of the most important trends in computing becau ~ making cc mputers behave intelligently is at least as important as manipulating data efficiently. Opportunities for using intelligent programs in NASA space station environments are numerous and abvicus. But many of those opportunities require substantial research in artificial intelligence before they can be realized. This _ paper looks at the technology of artificial intelligence, especially expert systems, to define "from the inside Out" what capabilities exist that are relevant for applications and environments In the space station, and what research needs to be promoted in order to achieve systems better able to interact symbiotically with a variety of persons for long times in space. Anderson and Chambers (1985) mention a number of characteristics of systems in a human-centered space station. These include: symbiosis with humans: human and machine capabilities may complement one another autonomous, continuing operation for a period up to 20 years, operating in an information-rich environment, consequent ~ of interactions with humans not entirely predictable, maturation of system implies flexibility to accommodate opera;~ion21 growth and minor up graces, evolution of system implies flexibility to acoom=odate new and enhanced functionality, system may include electrons c crew belters (ECHO), humans may have to learn new skills to interact productively with computers, computers may learn from humans, autonomous agents may serve a variety of roles with varying degrees of decision making power and authority. These are some of the relevant considerations in a top-5cwn design of systems for the space station. Each of these points implies a 113

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114 research and development program of some intensity. This paper takes a bottc m-up view of the same considerations--i.e., starts with what exists today and asks how we can achieve these design goals. By doing so, I hope to Introduce some relevant details into the design of Systems and the pi q of research. Expert systems are now being used in many decision-ma-ding situations of direct relevance to N~SA's mission, spanning manufacturing, engineering, medic me, and science. At present, they are used more as "intelligent assistants" than as replacements for technicians or experts. That is, they help people think through difficult problems and may provide suggestions about what to do, without taking over every aspect of the Cask. Cc mar programs that reason autoncmously are also of extreme importance in space, but they, too, must be integrated into an environment that is centered around people. They are extensions of present technology along several dimensions discussed here, that involve all of the same principles of design as the Intelligent assistant prc grams. One primary consideration is why intelligent systems are necessary in space. Although there are many reasons to build an expert system, they are all based on the premise: "Expertise is a scarce resource." The corollary (by Mhrphy's Law) is: "Even when there is enough expertise, it is never close enough to those who need it in a hurry." Because this is true--almost by definition of the term ' expertise '--constructing expert systems that reason at the level of N~SA's, or their contractors', specialists may have several benefits. These we summarized in Table 1. WHAT IS AN EXPERI SYSTEMn The general nature of expert systems is familiar to everyone within NASA. A reiteration of the four major characteristic is provided below to help define the most important dimensions for research and development efforts. An expert system is a computer program with expert-level problem solving abilities, which also fits some other criteria: it is a IABIE 1 Some Perceived Benefits of Expert Systems: Responses of 86 Users of Knowledge Engineering Tools 1. Replicate expertise 2. Preserve expertise 3. Increase productivity and cost savings 4. Free human experts for more demanding problems 5. Provide expert consultations to inexperience staff SOURCE: Bauman (1984)

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115 symbolic reasoning program that uses heuristics, its reasoning and knowledge base are understandable, and--most importantly--it is flexible. These characteristics are discussed below. All are important for applications in the space station, and all define research topics that will enhance current capabilities. Example One well-known expert system that has become a classic, although not actively used, is MYCIN. It was developed at Stanford by E. H. Shortliffe and others ~ the m~d-1970s. Its task is two-fold: (a) diagnose the cause ts) of infection in a patient and (b) recommend appropriate drug therapy. From a medical perspective, MYCIN's knowledge base is now dated; from the perspective of expert systems it represents much of the kind of reasoning that is captured in today's systems. MYCIN's conclusions were demonstrated to be equal in quality to those of infectious disease specialists at Stanford Medical Center. The sample typescript shown in Appendix A illustrates MYCIN's requesting information about a case and reason mg to conclusions about the best treatment. Performance Naturally we want computer programs to solve problems without error. But that is not always possible--in fact, outside of mathematics and logic we don't have flawless methods we can put into programs. Specialists in engineering, science, education, the m~litary--and every ares outside of pure logic--must solve problems with less than perfect methods. How do they do it? Mostly by building up specialized knowledge through extra years of training and experience and by reasoning carefully with that knowledge in situations they have learned to recognize. They are not infallible, though. Specialists' decisions are challenged frequently--most noti~=hly in the courts. So it is also unreasonable to expect computer programs to reason infallibly in all of these areas. Occasionally new methods are disocvered that provide much better results than the established methods of the old practitioners. But these improvements can then be put into prc grams, thus raising the overall standard of performance while still keeping the same relative standard of comparison with the best specialists. Reasoning When we say that expert systems are reasoning--and not just calculating with numters--we are saying that they belong to a class of programs using the methods of artificial intelligence thereafter AI). In the 1940s, computers were used almost exclusively for large mathematical problems. At Los Alamos, for instance, scientists had to solve complex mathematical equations in order to calculate elements in the design of

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116 the atomic bomb. These applications are usually referred to as large-scale scientific computation, or "number crunching" for short. In the 1950s, IBM and other computer manufacturers, realized the enormous value m helping b~-=iness solve problems of record keeping, payroll and the like. These applications extended the concept of comput~r-as-ca1culator to ccmputer-as-data-manager. In both of these classes of applications, the method of computation is error-free. There is no question that the result is correct, providing of course that the computer has been programmed correctly. A mathematical equation is solve] correctly; an employee roster is sorted correctly--if the Beth ~ are followed precisely. And computers are better able to follow complex instructions than people are. In computer science, logic an] mathematics we call these procedures algorithms. They a ~ procedures that can be guaranteed to provide a correct answer in a finite time, if there is one, and otherwise will provide a statement that the problem is not solvable. Some algorithms are too expensive to use, however, even in camputers. A classic example is finding the shortest route a Averting salesman can tat to visit many cities once and end up at home. With more than a hard of cities, aigorit~nic me~chc~s will not finish in time to be useful. For this reason, alternative methods have been developed. Art the mid l950s and early 1960s an alternative side of Sting ~ to be romanized as i~por~cant. Instead of always using aigori~ns, a Outer may case heuristics rules of thumb that aid In finding plausible answers quirky without guaranteeing the correctness of the results. Sc~t;~s these rules of throb are introduced into large primal simulations in order ~ get the situations ~ Card out anshrers more quickly. Or approximate methods may be sup tituted for more precise ones for the same reason. The assumptions may not all be correct; thus the results of the simulation may not be correct. When heuristic (non-algorithmic) methods are combined with symbolic (non-numeric) data, we are dealing with that part of computer science known as artificial intelligence. Understandability When someone truly knows something, he or she can "give an account" of what he knows. In our terms, good performance is not enough to call a person (or pro gram) an expert he/she (it) should also be able to explain why the solution is plausible, what features of the situation were noted to be important, what knowledge and problem solving methods were used. Otherwise we label a person ~~ "consistently (but unacocNnt~h~y) lucky", or maybe "psychic". Each field has its own standards of what a reasonable explanation is. A surgeon who recommends amputation of a leg generally talks about the process of disease or extent of injury and what will happen if it is not amputated. A broker So aWised liquidation of one's stock portfolio may explain the arrive with resent to technical Carts, historical trends, or sad ec~onc~nic principles that point to a stock market

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117 collapse. In their awn c~nities, both ache surgeon and the broker can usually justif~r--in hurt if necessary--the advice they give. Arxl we rears them as experts party because they have he ~a~riedge ached lets them do this. Flexibility We e; ~ experts to be flexible In their Inlet ~ . Arx] we regard persons as amateurs, not experts, when we encounter opinions that are rigid, locked-in ways of dealing with problems, or an inability to deal with new situations. In particular, there are two situations in which we want expert systems to be flexible: At advicergiving tamp we want the program (or a person) to provide good advice about situations that have never been encountered before. Novices with good memories may be able to provide the "textbook" answers for classic situations. Experts however, should, in addition, be able to reason about novel situations. 2. At the time a program is being constructed or modified (or a person is rearm ng), we want it to be flexible enough to assimilate new bodies of information. There should be a capacity for growth of knowledge, not a rigidity that freezes either the depth or breadth of the program's knowledge. SC ME APPIICAIIONS Some of the types of problems for which expert systems have been constructed are shown in Table 2. Many of these, such as small troubleshooting assistance programs, are relatively straightforward. Although the state of the art is difficult to quantify, the programs In the Tahoe represent the kinds of commercially robust systems that can be built for NASA today, provided adequate resources and an app striate problem. We don't have an adequate taxonomy of problem types. Many of these overlap, ~ being different forms of data interpretation, for example. Even this brief characterization, however, provides a reasonably good idea of what expert systems can do. In general, expert systems can reduce costs or increase quality of goods and services--in a single phrase, they can increase productivity in an organization. If you believe either that there is not enough expertise in the world, or that it is not well distributed, then you will be willing to entertain the idea that putting human expertise into an -~ily-replicated form may answer some productivity problems. Or, at least expert systems may provide a partial answer. Consider medical

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118 HE 2 Some Emblems for which Exam: Systems are ~vidi~ Solutions RISK ASSESSMENT E~I~ DAIS BEEN ADA En BEG It ~1']!;~'~ SO q~ ~Nr _~ . _ Ax . ~ : A; P~L10 ~ = ~U~ AURA S"1~;= CROP ~ # St. Paul Once Co.assess risk of insuring large camme~ial clients Genera ~tors~determi~ causes of vibration retie= and reccm=end repairs Digits Equipment Corp. translate customers' orders for computer sys ~ c into Hi Fin orders U. S. Army--design loading plan of cargo and equipment into aircraft of different types U. S. Air Force--plan an aircraft's route from base to target and back to avoid detection and threats when possible Schlumberger--~nterpret down-hole data from oil well bore holes to assist in prospecting Wasting house--plan manufacturing steps in a plant so as to avoid bottlenecks and delays Stanford Medical Center--~cist in managing malti-step chemotherapy for cancer patients IBM- monitor operations of MA operas mg sat U. S. Enr~ ion Agency--determine which requests for information fall under the exceptions to the Freedom of Information Act First Financial Planning Systems (Travelers Inch- analyze an indivi~sl's financial situation and ~ types of investments Hewlett Packard~iagnose causes of problems in potolith~ra~hy steps of wafer fabricatica, Virginia Polyphonic Institute - OCR for page 113
119 diagnosis. Specialists at university medical centers generally see more of the unusual disorders than a rural practitioner and thus stand a better chance of diagnosing them correctly. Putting some of that expertise more directly at the service of the rural practitioner could al low more effective treatment, and save patients the time and trouble of travel to the medical center. Or consider troubleshooting a complex piece of equipment. Persons with the most field experience are often the ones promoted to desk jobs in the central office. When subtle combinations of causes keep a less experienced field service technician from fixing a mechanic=] failure, someone with more expertise is needed. On earth, depending on travel times and =~he criticality of the work flow in the central office, calling the experienced specialist out may be a very expensive repair procedure. The following situations are all cases where it may make good sense to build an expert system: too few specialists for the number of problems; specialists not at the sites of problems when they occur; long training time for a specialist; high turnover among technicians; combination of complex equipment and poorly trained technicians; organization's best (or only) specialist in an area is nearing retirement; too many factors for a person to think through carefully in the time available. KEY CONCEPTS The four goals that characterize expert systems can be achieved with a few key methodological ideas. In this section, the key ideas will be introduced; in successive sections they will be elaborated on so as to explain a little how they work. The main organizational principle of expert systems is to keep specialized knowledge separate frog the logical and heuristic inference methods that use it. m is is -any to say but difficult to follow, for reasons that will be described later. Keep Dc main-Specific Knowledge Separate frog General Reasoning Methods -- FEY IDEA #1 -- Another key concept, which is imported from principled design of software generally, is modularity. (The first key idea is an instance of this, but that instance has taken on more importance than all the other instances of the general concept.) Modularity at the level of knowledge about the problem area implies conceptual separation of elements in the knowledge base. For example, medical knowledge about penicillin, although not totally independent, can often be separated from knowledge of other drugs. It can be codified in major ways, or deleted, without altering the program's knowledge of other drugs. So, this is to say that the concepts used to talk about objects ~ the Clan should be chosen so as to allow talking separately about an individual object, a single

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120 properly of an object, or a single relation of one type of object with another. Modularity at the level of programming constructs implies that the program's 1ntern21 representation of knowledge elements (e.g., objects, properties, relations) is similarly "clean". Keep independent pieces of knowledge independent. Keep the rest as nPa'ly-independent as possible. --KEY IDES #2 -- A third key concept is uniformity of conceptualization and representation of knowledge. The underlying intuition is that it is easier for a person or a program to build, understand, and codify a body of knowledge if it doesn't mix and merge a variety of different types of things. This is as true at the knowledge level as at the programming level. For instance, one of the most compelling aspects of Newbonts laws is that all physical bodies ore treated as quantities with mass. He didn't need one set of laws for planets and another for apples. So it is desirable to build an expert system with a "conceptually clean", well-organized, simple collection of concepts. And it is important to use a simple, well-organized collection of programming constructs as well. Otherwise there are too many different kinds of things to keep track of and reason with. There is more dispute among AI specialists about this principle. There are good reasons to violate it, as we shall see, ~ the interest of being able to say more about the objects and relations of interest than can conveniently be said in a single language. We are frequently told by bi-lingual friends, for instance, that there are some concepts that just can't be expressed fully in English. m e same is true for programming constructs, but the basic principle for constructing expert systems is to try to ma Attain uniformity as much as Possible. Strive for uniformity of language and programming constructs KEY IDES #3 A fourth principle is to design the expert system to mirror the ways experts think about problems in their dc mains. That means using the same terms and the same rules of reasoning as the experts use. One reason for this is that building and debugging a knowledge base depends necessarily on the expert, and using 1=== fa ~ liar terminology or methods will introduce confusion and error before the knowledge base is completed. Also, aft or it is completed it needs to be comprehensible and unambiguous to the practitioners using the system or else confusion and error will result. Note that we are assuming here that the expert designing the system knows how to make it understandable to users. Great care must be taken when building a system, however, to insure that this assumption is true. There are times when this principle will be, or should be, violated. For example, when efficient computer algorithms can solve part of a problem, it doesn't often make good sense to use anything else for that part, even if the experts don't think about it in that way.

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121 As huh as possible, he the sane vocabulary and methods In the program as the experts and practitioners use. -- KEY IDEA #4 -- These key ideas help us achieve all of our four goals in the following ways. HENCE In problems whose solution methods are not airea~r well formalized, which are considerable, Huh of the effort In building a knowledge base frees an expert system lies in building the conceptual frank. ~i~hpr~ies and relations of objects to describe is often not well specified at the beginning. So the Repledge base is built Duncan - lay, Here experience with one kna~riedge base guides future modifications, extensions, or reformations. RFASON~-~en the solution methods are not well characterize, it is important to encode heuristics that experts say they use. Storing these separately and In a simple form allows them to be changed easily. Since it is nearly impossible for an expert to articulate a complete and consistent set of heuristics at one sitting, it must be easy to add, remove, or modify the heuristics that determine the reasoning. UNDERSTAND~BILPTY--with modularity, individual elements of the knowledge base can be displayed meaningfully in isolation. Moreover, with the separation of knowledge base and inference prcce~ures it is possible to peruse the knowledge base in order to find just those elements that were used to reason about a new case. And with uniformity of data structures, it is possible to build one set of procedures that produce explanations. FLEX~BILITY--when the elements of the knowledge base are in separate data structures, and not intents med with code for inference procedures, we can add more knowledge with considerably more ease. When the indivi*u31 items in the knowledge base are nearly separate, we have fewer interactions to worry about when we change an items. And when the representation is homogeneous, we can more easily write other programs that act as "editing assistants" or explainers that help us insure correctness of new items and help us understand what is in the knowledge bit. ~RMANCE ISSUES Expert systems constitute one class of computer programs. As such, they work the same way as every other program: they process input data to produce output data. But the nature of the processing is different from most conventional programs. The key ideas motioned earlier are the key differences In the design am] irritation of expert systems. In order to design a rezoning program, we need to provide knowI~ge to radon with ark] reasoned methods to use. Both are needed. A

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122 powerful thinker needs something to think about, and a body of facts without methods for using them is sterile. Over the last few decades, research in AI has elucidated programmed y methods for making inferences and storing knowledge. We briefly characterize these topics below, although with some reservations about cversimplifying, in order to highlight research issues relevant to increasing the performance of expert systems. In addition to research on inference methods and representation of knowledge, several other issues are mentioned briefly as needing more research in order to improve the performance of expert systems. Inf~ren~- Methods Aristotle's Theory of the syllogism defined a~r~ptahie inference methods cutside of mathematics for about 2000 years. His theory has been extended in this century by Russell & Whitehe,ad, and others, in a formal theory that includes methods of reason ng with several statements and several variables in an argument. Formal logic defines several inference rules which are guaranteed to create true conclusions if the premises of the argument are true. The chain rule (mcdus ponens) is the single most important inference rule in expert systems. It allows us to chain together a string of inferences= If A then B If B then C If C then D A Many of the inferences we make In our lives are not guaranteed by the rules of logic, however, nor do we have certain knowledge about the truth of our premises. Whenever we argue that the future will be like the past, as in stock market predictions, we have to be prepared for exceptions. These inferences, labeled "plausible inferences" by George Polya, are the ones of most interest In AT. One set of p~r~ methods were In AI for making plausible inferences is to assert the facts categorically--as if they wee hood to be true with certa~n~r and then reason about exceptions that might force revisions to he conclusion. Anther set of naptha= deals explicitly with the degree- of unc~ca~n~r In the facts and In he associations. MYC]N (see Premix A) uses this style of neasonir~. Usually the degrees of uncertainty implied by words like "often" and ' ~ y" are expressed as numbers. And often these numbers are interpreted as probabilities. A third, and most powerful, set of methods is to introduce heuristic rules, or rules of plausible inference, into the reasoning. These are facts or relationships that are not guaranteed to produce correct conclusions, but will often do so. Moreover, they often produce answers more quickly than their algorithmic counterparts. In the

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123 traveling salesman problem, for example, the problem is to plan a route for visiting each cider in a set exactly once and ~ at the hcme cider. Ihis is an NP~nplete problem, that is, the algorithm for solving it Woes times that is exponential with ache number of cities. One heuristic we Tray ~n~uce is to go to the nearest city Scat has rot yet been visited. This certainly is up the computation of the route, but may (and probably will) miss the route that is shortest overall. Some mules of plausible inference used, with caution, In some exit systems are Shown below: Satisficing: If it will be expensive to find the very best solution to a problem, then stop with the first solution that satisfies easier criteria of being good enough. Inheritance: (Some specified) properties of a whole are shared by all its parts. E.g., An ice cube is cold and hard. Pieces of an ice cube are cold and hard. LBut other properties, like '~eight", do not behave the same.] Single Fault: If a piece of equipment (or any organized system) is malfunctioning, and one hypothesis explains the problem, then there prod ably is only a single cause of the problem. Compelling Evidence: If you have gathered a lot of evidence in favor of hypothesis Hi, and very little evidence against it, and you have gathered little positive evidence for alternative hypotheses, then HI is a plausible hypothesis. Dcccmposability: If there are many parts to a problem that are nearly independent, assume they can be solved independently. Then adjust the composed solution to take account of known interactions. Parsimony of Design: Designs or plans with fewer elements are preferred to those with more. In pr mciple, the rules of inference (both logical and plausible) may be applied again and again to a situation description, in any order, and the resulting conclusions will be the same. This is not always possible in practice, however. There may not be enough time to reason exhaustively about all possibilities and contingencies. For that reason AI researchers talk about controlling the inferences as being a more important, and more difficult, problem than making the inflect Controlling inferences breaks down into two subtasks: (a) deciding which rules to apply now, at this stage of the problem-solving process, and (b) deciding which part of the problem to work on now. Since we believe these sNbtasks require some intelligence, all of the principles for building knowledge-based systems also apply at this level of reason m g On participle, ~t is desirable to make this control knowledge explicit and separate from the inference methods

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131 Real Tim Monitoring As Herb systems beeline faster, it will be Tier to build systems ached monitor other devices or processes with rapid change. Conceptually a difficult problem is managing time~ependlent relations efficiently, which is one of the necessary ~ onents of a monitoring system. The large amounts of data received and the speed with which they are received are also critical issues. Integrating AI methods of reasoning about the data with numerical methods for digitizing and filtering is essential. Richer Ihput/Output No one likes to interact with computers by typing. Considerable work on interactive graphics has reduced the need for typing. But it will be even easier when we can communicate with programs by giving voice commands and receiving spoken English output in return. Models of Users and Situations No single style of interaction is best for all users at all times. Specialists do not need explanations of the meanings of terms, for example, while less experienced users used considerable help understanding the context of the problem. Also, the criticality of the situation may demand taking shortcuts in data acquisition or reasoning to reduce the risk immediately before taking a more systematic, detailed look at the problem. Expert systems must be sensitive to models of both the user and the situation in order to request appropriate input, reason at an appropriate level of detail, and present conclusions and suggestions in an appropriate way. CONCLUDING OBSERVATIONS Expert systems already are saving organizations millions of dollars and performing tasks routinely that ordinarily require human expertise. The number of applications of to~ay's technology is nearly boundless--consider, for example, the number of pieces of equipment in a space station that we don't readily know how to fix. The first commercial shells on the market are robust enough to be used effectively. Integrating ~nt=1ligent systems with conventional computer programs and with persons ~ the space station involve= new research in many dimensions. The single biggest advantage of AI programs, amply demonstrate in expert systems, is their flexibility. This matches precisely the single biggest design requirement on software in the space station. What we see now is just the beginning of ~ wave of intelligent software that can have as great an effect as business data prccessing software. It is impossible in any area of technology to make accurate

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132 predictions. However, there are many parallels between the growth of expert systems and or computing hardware, with about a 25-30 year lag. When electronic computers became available commercially, businessmen began to ask about applications that would make a difference to them. In 1955, several of these innovators assembled at Harvard to discuss they e~ien~-C. She of the conclusions they Mew falcon their party experience are summarized belay (Sheehan, 1955~: . 1. "The initial c~v~n~chusiasm, which inevitably Zanies a project of this scope, can and does make the job harder. Too many people had the impression that this was the answer to all Problems. Plans it is. but we haven't bed sprat enough to . . develop all of them... , 2. "Some of cur original thinking has been partly confirmed in that the greatest benefits to be derived from a computer will probably consist of information impossible to obtain previously... 3. "Our experience has shown that the compute' is more adaptable to some projects than others... 4. "Programmers should be recruited within your own company...It is easier to teach men the required computer and program techniques than to acquaint them properly with the complex pracedurcs and routines of mcdern-day industry... 5. "I doubt if it is possible to overemphasize the desirability of providing for convenient corrections or deletion of errors in data. . . 6. 'the maximum justifiable amount of flexibility for extending or integrating applications must be included in the initial programming..." The parallels with expert systems are clear: we have just begun to explore the first set of applications an] have only a limited view of possibilities in the future. Over the next 5-10 years, however, we do know what extensions in capabilities will be possible because they are the ones that are active research areas in the present. Future developments will make the technology more broadly applicable and easier to use. Every professional activi~y--from medicine to manufacturing--contains many problem solving tasks that are not mathematical. The develcpment of artificial intelligence methods that can solve many of these kinds of problems will change the nature of applications of computers in cur society as much as the develcpment of transistors changed the a architecture and applicability of computers themselves.

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133 Appendix: Annotates! Typescript of a Sample MYCIN Consultation Instructions for Users at the Start of an A. 1 Infectious Disease Consultation (Optional) [Here, as elsewhere. tore responses hyped by the user follow a double-asterisk prompt. MYCIN 3-Jun-78 . . . Special options (type ? for help): . Instructions? (y or N) YES This is a computer program named MYCIN that has been designed to advise you regarding an appropriate choice of infectious disease therapy. it is understood that you have a patient who may have an infection. Please answer the following questions, terminating each response with RETURN. To correct typing errors. use the DELETE key to delete single characters, ~ctrl>W to delete a word, and Q to delete the whole line. If you are not certain of your answer, you may modify the response by inserting a certainty factor (a number from 1 to 10) in parentheses after your response. Absolute certainty (10) is assumed for every unmodified answer It is likely that some of the following questions cannot be answered with certainty. You may change an answer to a previous question in two ways. If the program is waiting for a response from you (that is, has typed ), enter CHANGE followed by the number(s) of the question(s) whose answers will be altered. You may also change a previous answer at any time (even when the program is not waiting for a response from you) by typing cctr1>F (Fix), which will cause the program to interrupt its computation and ask what you want to change. (If the response to F is not immediate. try typing the RETURN key in addition.) Try to avoid going back because the process requires reconsidering the patient from the beginning and therefore may be slow. Note that you may also enter UNK (for UNKnown) if you do not know the answer to a question, ? if you wish to see a more precise definition of the question or some examples of recognized responses. ?? if you want to see all recognized responses, the word RULE if you would like to see the decision rule which has generated the question being asked. the word WHY if you would like to see a more detailed explanation of the question. or the letters QA if you would like to interrupt the consultation in order to ask questions regarding the decisions made so far In the consultation. If you are ever puzzled about what options are available to you during a consultation. enter the word HELP and a list of options will be listed for you. Sample Response [user input follows the 1 Does the patient have a risk factor for tuberculosis?

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134 Appendix: Annotated Typescript of a Sample MYCIN Consultation One or more of the following are considered risk factors for TB: a) positive PPD (5TU). by history of close contact with a person having active TB. c) household member with a past history of active TB. d) chest X-rav showing apical scarring. e) granulomas seen on biopsy of any organ tissue. expected responses are: YES NO Ente! HELP for user options. - YES SUMMARY: (type ctrl-O to abort printout) UNK - Answer not known. ? - Rephrases the question and gives examples of recognized responses. ?9 - Prints a list of all recognized responses. Prints the current decision rule. Program enters question-answering mode. Go back and re-request answer to question number #. Enter comments about any aspect of the system s performance. Your comments will be forwarded to those in charge of the MYCIN program. Gives high-level explanation of the current reasoning chain that provoked this question Explains HOW the system wii achieve a goal referred to by number # in a previous explanation. Provides a more detailed explanation of a previous answer given by a WHY command Resets the explanation of the reasoning chain back to lowest level. as if you never used the WHY EXPLAIN commands. Halts the program without completing the consultation. Halts the program saving the current patient on a disk file retrievable at a later date. Prints this list. RULE - QA - CHANGE # COMM ENT WHY - EXPLAIN FORGET - STOP - SAVE HELP A.2 Infectious Disease Consultation Session [This is a typescript of a MYCIN consu/tabon on an actua/ patient treated at Stanford MYCIN was no' used to suggest treatment. Spaces have been added for readability. and comments (in italics) have been inserted Otherwise it is a verbatim transcript. Users responses follow double asterisks ----PATIENT-538-------- 1 ) Pat~ent s name: 2' l ., PT538 Age: 34 YEARS 3) Sex: MAEL = MALE 4) Race: WHY [i.e. WHY is it important to determine the race of PATIENT-538?] [Name age. sex. and race are always asked because they are INlTlALDA`-A j [MYCIN corrects misspellings [The user asks for an explanation In this Instance it is trivia! [1.0] The race of a patient is one of the 5 standard parameters that identify a patient. This may be useful later in the consultation. For example. it Is relevant In determining the organisms (other than those seen on cultures or smears) which might be causing the Infection. [back to question 4...l CAUCASIAN Please supply one answer under each heading. If your response Is not fans is the answer to the quest/on about race

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135 Appendix: Annotated Typescript of a Sample MYCIN Consultation 16) Please give the date on which clinical evidence (symptoms. signs. or laboratory tests) of the meningitis first appeared. 29-Jan-77 22:15 17j Does Pt538 have an abnormal chest X-ray? NO 18) Does Pt538 have one or more of the following risk factors for TB: a) positive intermediate strength PPD b) history of close contact with a person with active TB. c) a household member with a past history of active TB d) apical scarring on chest X-ray. e) history of granulomas NO 19) Does Pt538 have any cutaneous lesions or rash on physical examination? NO 20) Does Pt538 have evidence of ocular nerve dysfunction? NO 21) is Pt538 a compromised host (e.g. alcoholic. sickle-cell-disease. immunosuppressed)? YES 22) Is Pt538 ~mmunosuppressed (e 9 corticosteroid therapy. cytotoxic drug therapy. radiation therapy. leukemia. Iymphoma)? N<:) 23) For how many days has PtS38 had abnormal necrologic signs? On biopsy of liver lymph node. or other organs? 7.2 HOURS 24) For how many days has Pt538 had necrologic symptoms? ~ DAYS 25) Has PtS38 had recent evidence of tender or enlarged salivary glands? '' NC 26) Has Pt538 been exposed to any contagious disease recently (e.g. men~ngococcal disease. mumps) ? NO iNote that the answer was given in hours. MYCIN has to be flexible enough to convert un~ts.J Please enter CSf findings In the following table CSF Percent Protein CSF Peripheral WBC PMN Glucose Glucose 27) 100 99 280 5 UNKNOWN 1Note that some information is missJng.j 28) Do you know the results of a recent CBC of PtS38? . YES Please enter CE3C results in the following table WBC Percent Percent In 1000s PMN BANDS 29) 1.9 UNKNOWN UNKNOWN 30) Has Pt538 had any InJury or insult to. or defect in the central- nervous-system (excluding neurosurgery)? NO 31) Has PtS38 ever undergone any type of surgery? - YES 32) Does PtS38 have a history of NEUROSURGERY? ' M0 33) Does PtS38 I've in a crowded environments NO 34! Do you suspect recent alcoholic history In Pt538? NO 35) is meningitis a hospital-acqu~red Infection? WHY [This is a more interesting question. requ/rmg presentation of the rule that caused question 35 to be asked. Note that the first five clauses of the rule have been ~nvesngated and question 35 IS motivated by the last clause. MYCIN first rephrases the question tO give the user its ,nterpretanon of the WHY|

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36 Infectious Disease Consultation Session (i e. WHY is it important to determine whether the Infection was acquired while the patient was hospitalized?] [2.01 This will aid in determining the organisms (other than those seen on cultures or smears! which rn~gnt be causing the Infection It has already been established that Author: Yu [RULE5453 [back to question 35. HOW 2.3 [2 1 J the infection which requires therapy Is meningitis and [2 21 organisms were not seen on the stain of this pending CSF culture. and [2 3] the type of the infection is bacterial. and [2.41 neurosurgery is not one of the relevant items from the history of Pt538. and [2.5] the age of Pt538 is greater than 10 days Therefore. if [2.6] the infection was acquired while the patient was hospitalized then there is evidence that the organisms (other than those seen on cultures or smears) which might be causing the Infention is e cold ( 75) staphylococcus-coag-pos ( 3) pseudomonas-aerug!nosa ( 3) klebsiella-pneumoniae (.5) {MYCIN now requests the answer to question 35 again. Instead of answering. the user asks how clause 2.3 was confirmed.] [i.e. HOW was it established that the type of the infection is bacterial?] [Ramer than printing the contents of the rules. MYCIN shows the rule numbers and the strength of positive and negative evidence from each rule.| The following rules concluded about: the likelihood that the type of the infection is bacterial Rule Yes No [2.71 RULE148 (.19) [2.8] RULE500 (.51) [2.91 RULE501 (.95) [3.0] RULE502 (.97) [3.1] RULE526 ( 98) (3.2] RULE504 (.97) [3.31 RULE524 ( 98) Thus it has been established that the type of the infection is bacterial. back to question 35... HOW 2.5 [i.e. HOW was it established that the age of pt538 is greater than 10 days? In answer to question 2 you said that the age of pt538 is 34 years Thus it has been established that the age of pt538 is greater than 10 days. [back to question 35. . .] ~~ y 36) Is Pt538 s clinical history consistent with EPIGLOTTITiS? NO 37) Is Pt538s clinical history consistent with OTITIS-MEDIA? ~ NO 38) Has Pt538 ever undergone splenectomy? NO 39) Is Pt538 a burn patient? WHY [Another explanation is reglJested.

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137 Appendix: Annotated Typescript of ~ Sample MYCIN Consultation [i.e. WHY is it important to determine whether PATIENT-538 has been seriously bumed?l 4.01 This will aid in determining the organisms (other than those seen on cultures or smears) which might be causing the infection. It has already been established that [4.1] the infection which requires therapy is meningitis and [4.21 organisms were not seen on the stain of this pending CSF culture. and [4.3] the type of the infection is bacterial Therefore. if then 14.41 pt538 has been seriously burned there is suggestive evidence (.5) that pseudomonas-aeruginosa is one of the organisms (other than those seen on cultures or smears) which might be causing the infection Author: Yu [RULE578] [back to question 39. . .] WHY [In this case the user asks another WHY question. backing up the subgoal tree.J [i.e. WHY is it important to determine the organisms (other than those seen on cultures or smears) which might be causing the infection?] [5.0] ... in order to determine the therapeutic regimen of PATIENT-538. If [5.1 ] information has been gathered about organisms isolated from the patient organisms noted on smears of the patient negative cultures of the patient. suspected infections without microbiological evidence. current drugs of the patient and prior drugs of the patient and 5.2] an attempt has been made to deduce the organisms which require therapy and [5.3] You have given consideration to organisms (other than those seen in cultures or smears) which might be present and found at least one organism which requires therapy. then determine the best therapy recommendation from among the drugs likely to be effective against the significant organisms tRULE092] [back to question 39. . .] ~~ y 1MYCIN now prints its intermediate conclusions about the likely causes of the infection.] I have found evidence (RULE545 RULE557) that treatment should cover for E.coli causing meningitis. I have found evidence (RULE545 RULE557 RULE578) that treatment should cover for Pseudomonas-aerug~nosa causing meningitis. I have found evidence (RULES45 RULE557) that treatment should cover for Klebsielia-pneumoniae causing meningitis. I have found evidence (RULE507) that treatment should cover for Diplococcus- pneumoniae causing meningitis. [Now MYCIN begins to ask questions related to appropriate theraPyJ 40) Does PtS38 have focal neurological signs? UNKNOWN Some questions are motivated by the organism under consideration. Thus the following lines sometimes provide useful contextua/ information to a users (Determining which drugs are desirable for use against the E.coli. . .] [Determining which drugs are desirable for use against the Pseudomonas- aerug~nosa. .

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138 [Determining which drugs are desirable for use against the F KLEBSIELLA-PNEUMONIAE [from clinical evidence only - ITEM-4:> DIPLOCOCCUS-PNEUMONIAE [from clinical evidence only 41) Does PtS38 have a clinically significant allergic reaction to any antimicrobial agent? NO [Considering AMPICILLIN-AND-GENTAMICIN for use against INFECTION-~...J 42) Do you have reason to suspect that PtS38 may have impaired renal function? NO 43) Pt538s weight in kilograms (or POUNDS): 70 Finally, MYCIN prints the best therapy it has found to maximize the chances of effective coverage of the caused of the infection while minimizing the number of drugs, their toxicity, and side effects! REC-l] 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: 3.59 (28.0 ml) q4h IV [calculated on basis of 50 mg kgJ 2) GENTAMICIN Dose: 119 mg (3.0 ml 80mg!2ml ampule) q8h IV [calculated on basis of 1.7 mgJkg plus consider giving 5 mg q24h Intrathecal Comments: Monitor serum concentrations Since high concentrations of penicillins can inactivate aminoglycosides do not mix these two antibiotics in the same IV bottle. Do you wish to see the next choice therapy? YES Which. if any of tt" above drugs do you wish eliminated from consideration? AMPICILLIN [Considering GENTAMICIN for use against INFECTION-t...] [Conskbring PENICILLIN for use against INFECTION-1...1 [REC-21 Next best therapy recommendation: In order to cover for Items <1 2 3~: Give: GENTAMICIN Dose: 119 mg (3.0 ml 80mg/2ml ampule) q8h IV [calculated on basis of 1.7 m~kgl plus consider giving 5 mg q24h Intrathecal Comments: Monitor serum concentrations In order to cover Or Item : Give: PENICILLIN Dose: 3.5 million units (7.0 ml) q4h IV [calculated on basis of 50000 unitsJks, tOther therapies can be examined 1

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139 understood. you will be prompted by the standard questioning method. (enter ? for normal prompting, for questions that are not applicable ) Please enter the results of positive cultures using the following table. site culture date dated number collected positive 5) DONE Please enter the results of pending cultures in the following table. site culture date stain number collected examined 6) ' CSF 1234 30-Jan-77 YES Infectious Disease Consultation Session method of collection [urine,sputum only [The next question asks for severe/ items in tabular form since they are found together in the hospital record. method ofl collection [urine,sputum only| stain of morph! fMYClN follows up on the stain of smear the smear and the morphology.! [type DONE if no organism was seen on the smear 7)--DONE [No information about stain or morphology is available. MYCIN now follows up question 6, asking about other positive cuJtures. site cul # date coil. smear coil. ~nothodl examined?! 8)''DONE [There are no more positive cu/tures.1 Please enter negative cultures in the following table. site culture dated method of number collected collection [urine,sputum only] 9)--DONE 10) Do you suspect Pt538 may have an infection at a site from which you have not obtained culture specimens? NO Please enter information on current antimicrobials. drug route of dater name administration started 11). CEPHALOTHIN IV 23-Jan-77 1 2) 'DONE Please enter information on prior antimicrobials. drug route of name 1 3)--DONE dater administration started Considering organisms (other than those isolated from cultures) which might be print. . .] 14) Has Pt538 recently had symptoms of persistent headache or other abnormal necrologic symptoms (dizziness, lethargy, etc.)? YES 15) Has Pt538 recently had objective evidence of abnormal neurologic signs (nuchal rigidity, coma, seizures, etc.) documented by physician observation or examination? ~ YES The CSF cultures will be considered to be associated with MENINGITIS. [There are no negative cuitures.J dater stopped

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140 Appendix: Annotated Typescript of a Sample MYCIN Consultation RECOMMENDATION-2 is equivalent to flECOMMENDATION.1. Would you like to see details of this comparison? YES Looking at the coverage of the 'most likely' items, we see that: RECOMMENDATION-1 consists of 1 first choice drug (AMPICILLIN for ITEM-1 and ITEM - ) and 1 third choice drug (GENTAMICIN for ITEM-1, ITEM-2 and ITEM-3) RECOMMENDATION-2 consists of 1 first choice drug (PENICILLIN for ITEM-4) and 1 third choice drug (GENTAMICIN for ITEM-2. ITEM-1 and ITEM-3) ORGANISMS RECOMMENDATION-1 RECOMMENDATION-2 most likely' Drug -- Choice Ranking Drug -- Choice Ranking ITEM-1 AMPICILLIN-AND-GENTAMICIN--1 st GENTAMICIN--3rd ITEM-2 GENTAMICIN--2nd GENTAMICIN--2nd ITEM-3 GENTAMICIN--3rd GENTAMICIN--3rd ITEM-4 AMPICILLIN--1st PENICILLIN--1st (The desirability of a drug is defined to be its lowest ranking for the items it covers.) Do you wish to see the next choice therapy? ' NO

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141 RENGF~ Arxierson, J. L. and A. Cl~. 1985 ~nan~nter~ Space Station Design. Paper presented at Conference on Cc - uters ~ Aerospace, Lord Beadh, CA, October 21-23. Bagman, B. 1984 MOP Thesis, University of Texas, Or. Buchanan, B. G. 1986 Expert systems: working systems and the Church literature. Expert Systems 3 (1) :32-51. Also appear as S`canford REL Report 85-37 and SON CS-85-1075. Han, Paul. 1986 ~ventor)~ and analysis of existing expert system. Expert S:rst~; Strategies 2 (8): 1-16. Miller, R. A., Pople, H. E. and M\rers, J. D. 1982 ~NTERNTST-1, Art Experiments ~ter-Based Diagnostic Consultant for General Thternal Mectic~ne. N=M 307(8) :468-476. peso ahead; in 'Readings In Medical Artificial Intelligence: The First Decade', Clingy. W. J and Shortliffe, E. H., Ad., Wading, Ha, A~ison-Wesley, Pp. 190-209. Sheehan, G. M 1955 An application to payroll. Data processing Conference. Cambridge, He, September. . Feedings of the Autistic Heard University ess,