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SOME EXPERT SYSTEM NEED COMMON

SENSE

John McCarthy

Computer Science Department

Stanford University

Stanford, CA 94305

jmc@cs.stanford.edu

http://www-formal.stanford.edu/jmc/

Abstract

An expert system is a computer program intended to embody the knowl-

edge and ability of an expert in a certain domain. The ideas behind them and

several examples have been described in other lectures in this symposium.

Their performance in their specialized domains are often very impressive.

Nevertheless, hardly any of them have certain common sense knowledge and

ability possessed by any non-feeble-minded human. This lack makes them

“brittle”. By this is meant that they are difficult to extend beyond the scope

originally contemplated by their designers, and they usually don’t recognize

their own limitations. Many important applications will require common

sense abilities. The object of this lecture is to describe common sense abili-

ties and the problems that require them.

Common sense facts and methods are only very partially understood to-

day, and extending this understanding is the key problem facing artificial

intelligence.

This isn’t exactly a new point of view. I have been advocating “Com-

puter Programs with Common Sense”since I wrote a paper with that title in

1958. Studying common sense capability has sometimes been popular and

sometimes unpopular among AI researchers. At present it’s popular, per-

haps because new AI knowledge offers new hope of progress. Certainly AI

researchers today know a lot more about what common sense is than I knew

in 1958 — or in 1969 when I wrote another paper on the subject. However,

expressing common sense knowledge in formal terms has proved very difficult,

and the number of scientists working in the area is still far too small.

One of the best known expert systems is MYCIN (Shortliffe 1976; Davis,

Buchanan and Shortliffe 1977), a program for advising physicians on treating

bacterial infections of the blood and meningitis.

It does reasonably well

without common sense, provided the user has common sense and understands

the program’s limitations.

MYCIN conducts a question and answer dialog. After asking basic facts

about the patient such as name, sex and age, MYCIN asks about suspected

bacterial organisms, suspected sites of infection, the presence of specific

symptoms (e.g. fever, headache) relevant to diagnosis, the outcome of labo-

ratory tests, and some others. It then recommends a certain course of antibi-

otics. While the dialog is in English, MYCIN avoids having to understand

freely written English by controlling the dialog. It outputs sentences, but the

user types only single words or standard phrases. Its major innovations over

many previous expert systems were that it uses measures of uncertainty (not

probabilities) for its diagnoses and the fact that it is prepared to explain its

reasoning to the physician, so he can decide whether to accept it.

Our discussion of MYCIN begins with its ontology. The ontology of a

program is the set of entities that its variables range over. Essentially this is

what it can have information about.

MYCIN’s ontology includes bacteria, symptoms, tests, possible sites of in-

fection, antibiotics and treatments. Doctors, hospitals, illness and death are

absent. Even patients are not really part of the ontology, although MYCIN

asks for many facts about the specific patient. This is because patients aren’t

values of variables, and MYCIN never compares the infections of two differ-

ent patients. It would therefore be difficult to modify MYCIN to learn from

its experience.

MYCIN’s program, written in a general scheme called EMYCIN, is a so-

called production system. A production system is a collection of rules, each

of which has two parts — a pattern part and an action part. When a rule

is activated, MYCIN tests whether the pattern part matches the database.

If so this results in the variables in the pattern being matched to whatever

entities are required for the match of the database. If not the pattern fails

and MYCIN tries another. If the match is successful, then MYCIN performs

the action part of the pattern using the values of the variables determined

by the pattern part. The whole process of questioning and recommending is

built up out of productions.

The production formalism turned out to be suitable for representing a

large amount of information about the diagnosis and treatment of bacterial

infections. When MYCIN is used in its intended manner it scores better than

medical students or interns or practicing physicians and on a par with experts

in bacterial diseases when the latter are asked to perform in the same way.

However, MYCIN has not been put into production use, and the reasons given

by experts in the area varied when I asked whether it would be appropriate to

sell MYCIN cassettes to doctors wanting to put it on their micro-computers.

Some said it would be ok if there were a means of keeping MYCIN’s database

current with new discoveries in the field, i.e. with new tests, new theories,

new diagnoses and new antibiotics. For example, MYCIN would have to

be told about Legionnaire’s disease and the associated Legionnella bacteria

which became understood only after MYCIN was finished. (MYCIN is very

stubborn about new bacteria, and simply replies “unrecognized response”.)

Others say that MYCIN is not even close to usable except experimen-

tally, because it doesn’t know its own limitations. I suppose this is partly a

question of whether the doctor using MYCIN is trusted to understand the

documentation about its limitations. Programmers always develop the idea

that the users of their programs are idiots, so the opinion that doctors aren’t

smart enough not to be misled by MYCIN’s limitations may be at least partly

a consequence of this ideology.

An example of MYCIN not knowing its limitations can be excited by

telling MYCIN that the patient has Cholerae Vibrio in his intestines. MYCIN

will cheerfully recommend two weeks of tetracycline and nothing else. Pre-

sumably this would indeed kill the bacteria, but most likely the patient will

be dead of cholera long before that. However, the physician will presumably

know that the diarrhea has to be treated and look elsewhere for how to do

it.

On the other hand it may be really true that some measure of common

sense is required for usefulness even in this narrow domain. We’ll list some

areas of common sense knowledge and reasoning ability and also apply the

criteria to MYCIN and other hypothetical programs operating in MYCIN’s

domain.

1 WHAT IS COMMON SENSE?

Understanding common sense capability is now a hot area of research in

artificial intelligence, but there is not yet any consensus. We will try to

divide common sense capability into common sense knowledge and common

sense reasoning, but even this cannot be made firm. Namely, what one man

builds as a reasoning method into his program, another can express as a fact

using a richer ontology. However, the latter can have problems in handling

in a good way the generality he has introduced.

2 COMMON SENSE KNOWLEDGE

We shall discuss various areas of common sense knowledge.

  1. The most salient common sense knowledge concerns situations that change in time as a result of events. The most important events are actions,

    and for a program to plan intelligently, it must be able to determine the

    effects of its own actions.

    Consider the MYCIN domain as an example. The situation with which

    MYCIN deals includes the doctor, the patient and the illness. Since MYCIN’s

    actions are advice to the doctor, full planning would have to include infor-

    mation about the effects of MYCIN’s output on what the doctor will do.

    Since MYCIN doesn’t know about the doctor, it might plan the effects of

    the course of treatment on the patient. However, it doesn’t do this either.

    Its rules give the recommended treatment as a function of the information

    elicited about the patient, but MYCIN makes no prognosis of the effects of

    the treatment. Of course, the doctors who provided the information built

    into MYCIN considered the effects of the treatments.

    Ignoring prognosis is possible because of the specific narrow domain in

    which MYCIN operates. Suppose, for example, a certain antibiotic had

    the precondition for its usefulness that the patient not have a fever. Then

    MYCIN might have to make a plan for getting rid of the patient’s fever and

    verifying that it was gone as a part of the plan for using the antibiotic. In

    other domains, expert systems and other AI programs have to make plans,

    but MYCIN doesn’t. Perhaps if I knew more about bacterial diseases, I

    would conclude that their treatment sometimes really does require planning

    and that lack of planning ability limits MYCIN’s utility.

    The fact that MYCIN doesn’t give a prognosis is certainly a limitation.

    For example, MYCIN cannot be asked on behalf of the patient or the admin-

    istration of the hospital when the patient is likely to be ready to go home.

    The doctor who uses MYCIN must do that part of the work himself. More-

    over, MYCIN cannot answer a question about a hypothetical treatment, e.g.

    “What will happen if I give this patient penicillin?” or even “What bad

    things might happen if I give this patient penicillin?”.

  2. Various formalisms are used in artificial intelligence for representing facts about the effects of actions and other events. However, all systems that

    I know about give the effects of an event in a situation by describing a new

    situation that results from the event. This is often enough, but it doesn’t

    cover the important case of concurrent events and actions. For example, if

    a patient has cholera, while the antibiotic is killing the cholera bacteria, the

    damage to his intestines is causing loss of fluids that are likely to be fatal.

    Inventing a formalism that will conveniently express people’s common sense

    knowledge about concurrent events is a major unsolved problem of AI.

  3. The world is extended in space and is occupied by objects that change their positions and are sometimes created and destroyed. The common sense

    facts about this are difficult to express but are probably not important in

    the MYCIN example. A major difficulty is in handling the kind of partial

    knowledge people ordinarily have. I can see part of the front of a person in

    the audience, and my idea of his shape uses this information to approximate

    his total shape. Thus I don’t expect him to stick out two feet in back even

    though I can’t see that he doesn’t. However, my idea of the shape of his back

    is less definite than that of the parts I can see.

  4. The ability to represent and use knowledge about knowledge is often required for intelligent behavior. What airline flights there are to Singapore

    is recorded in the issue of the International Airline Guide current for the

    proposed flight day. Travel agents know how to book airline flights and can

    compute what they cost. An advanced MYCIN might need to reason that Dr.

    Smith knows about cholera, because he is a specialist in tropical medicine.

  5. A program that must co-operate or compete with people or other pro- grams must be able to represent information about their knowledge, beliefs,

    goals, likes and dislikes, intentions and abilities. An advanced MYCIN might

    need to know that a patient won’t take a bad tasting medicine unless he is

    convinced of its necessity.

  6. Common sense includes much knowledge whose domain overlaps that of the exact sciences but differs from it epistemologically. For example, if I

    spill the glass of water on the podium, everyone knows that the glass will

    break and the water will spill. Everyone knows that this will take a fraction

    of a second and that the water will not splash even ten feet. However, this

    information is not obtained by using the formula for a falling body or the

    Navier-Stokes equations governing fluid flow. We don’t have the input data

    for the equations, most of us don’t know them, and we couldn’t integrate

    them fast enough to decide whether to jump out of the way. This common

    sense physics is contiguous with scientific physics. In fact scientific physics is

    imbedded in common sense physics, because it is common sense physics that

    tells us what the equation s = 0.5gt2 means. If MYCIN were extended to be

    a robot physician it would have to know common sense physics and maybe

    also some scientific physics.

    It is doubtful that the facts of the common sense world can be represented

    adequately by production rules. Consider the fact that when two objects

    collide they often make a noise. This fact can be used to make a noise,

    to avoid making a noise, to explain a noise or to explain the absence of a

    noise. It can also be used in specific situations involving a noise but also to

    understand general phenomena, e.g. should an intruder step on the gravel,

    the dog will hear it and bark. A production rule embodies a fact only as part

    of a specific procedure. Typically they match facts about specific objects,

    e.g. a specific bacterium, against a general rule and get a new fact about

    those objects.

    Much present AI research concerns how to represent facts in ways that

    permit them to be used for a wide variety of purposes.

    3 COMMON SENSE REASONING

    Our ability to use common sense knowledge depends on being able to do

    common sense reasoning.

    Much artificial intelligence inference is not designed to use directly the

    rules of inference of any of the well known systems of mathematical logic.

    There is often no clear separation in the program between determining what

    inferences are correct and the strategy for finding the inferences required to

    solve the problem at hand. Nevertheless, the logical system usually corre-

    sponds to a subset of first order logic. Systems provide for inferring a fact

    about one or two particular objects from other facts about these objects and

    a general rule containing variables. Most expert systems, including MYCIN,

    never infer general statements, i.e. quantified formulas.

    Human reasoning also involves obtaining facts by observation of the world,

    and computer programs also do this. Robert Filman did an interesting thesis

    on observation in a chess world where many facts that could be obtained by

    deduction are in fact obtained by observation. MYCIN’s doesn’t require this,

    but our hypothetical robot physician would have to draw conclusions from a

    patient’s appearance, and computer vision is not ready for it.

    An important new development in AI (since the middle 1970s) is the

    formalization of nonmonotonic reasoning.

    Deductive reasoning in mathematical logic has the following property —

    called monotonicity by analogy with similar mathematical concepts. Sup-

    pose we have a set of assumptions from which follow certain conclusions.

    Now suppose we add additional assumptions. There may be some new con-

    clusions, but every sentence that was a deductive consequence of the original

    hypotheses is still a consequence of the enlarged set.

    Ordinary human reasoning does not share this monotonicity property. If

    you know that I have a car, you may conclude that it is a good idea to ask

    me for a ride. If you then learn that my car is being fixed (which does not

    contradict what you knew before), you no longer conclude that you can get

    a ride. If you now learn that the car will be out in half an hour you reverse

    yourself again.

    Several artificial intelligence researchers, for example Marvin Minsky (1974)have pointed out that intelligent computer programs will have to reason non-

    monotonically. Some concluded that therefore logic is not an appropriate

    formalism.

    However, it has turned out that deduction in mathematical logic can be

    supplemented by additional modes of nonmonotonic reasoning, which are just

    as formal as deduction and just as susceptible to mathematical study and

    computer implementation. Formalized nonmonotonic reasoning turns out to

    give certain rules of conjecture rather than rules of inference — their conclu-

    sion are appropriate, but may be disconfirmed when more facts are obtained.

    One such method is circumscription, described in (McCarthy 1980).

    A mathematical description of circumscription is beyond the scope of

    this lecture, but the general idea is straightforward. We have a property

    applicable to objects or a relation applicable to pairs or triplets, etc. of

    objects. This property or relation is constrained by some sentences taken

    as assumptions, but there is still some freedom left. Circumscription further

    constrains the property or relation by requiring it to be true of a minimal set

    of objects.

    As an example, consider representing the facts about whether an object

    can fly in a database of common sense knowledge. We could try to provide

    axioms that will determine whether each kind of object can fly, but this

    would make the database very large. Circumscription allows us to express

    the assumption that only those objects can fly for which there is a positive

    statement about it. Thus there will be positive statements that birds and

    airplanes can fly and no statement that camels can fly. Since we don’t include

    negative statements in the database, we could provide for flying camels, if

    there were any, by adding statements without removing existing statements.

    This much is often done by a simpler method — the closed world assumption

    discussed by Raymond Reiter. However, we also have exceptions to the

    general statement that birds can fly. For example, penguins, ostriches and

    birds with certain feathers removed can’t fly. Moreover, more exceptions may

    be found and even exceptions to the exceptions. Circumscription allows us

    to make the known exceptions and to provide for additional exceptions to be

    added later — again without changing existing statements.

    Nonmonotonic reasoning also seems to be involved in human communi-

    cation. Suppose I hire you to build me a bird cage, and you build it without

    a top, and I refuse to pay on the grounds that my bird might fly away. A

    judge will side with me. On the other hand suppose you build it with a top,

    and I refuse to pay full price on the grounds that my bird is a penguin, and

    the top is a waste. Unless I told you that my bird couldn’t fly, the judge will

    side with you. We can therefore regard it as a communication convention

    that if a bird can fly the fact need not be mentioned, but if the bird can’t fly

    and it is relevant, then the fact must be mentioned.

    References

    Davis, Randall; Buchanan, Bruce; and Shortliffe, Edward (1977). Production

    Rules as a Representation for a Knowledge-Based Consultation Program,

    Artificial Intelligence, Volume 8, Number 1, February.

    McCarthy, John (1960). Programs with Common Sense, Proceedings of the

    Teddington Conference on the Mechanization of Thought Processes, London:

    Her Majesty’s Stationery Office. (Reprinted in this volume, pp. 000–000).

    McCarthy, John and Patrick Hayes (1969). Some Philosophical Problems

    from the Standpoint of Artificial Intelligence, in B. Meltzer and D. Michie

    (eds), Machine Intelligence 4, Edinburgh University.

    (Reprinted in B. L.

    Webber and N. J. Nilsson (eds.), Readings in Artificial Intelligence, Tioga,

    1981, pp. 431–450; also in M. J. Ginsberg (ed.), Readings in Nonmonotonic

    Reasoning, Morgan Kaufmann, 1987, pp. 26–45; also in this volume, pp.

    000–000.)

    McCarthy, John (1980). Circumscription — A Form of Nonmonotonic Rea-

    soning, Artificial Intelligence, Volume 13, Numbers 1,2. (Reprinted in B. L.

    Webber and N. J. Nilsson (eds.), Readings in Artificial Intelligence, Tioga,

    1981, pp. 466–472; also in M. J. Ginsberg (ed.), Readings in Nonmonotonic

    Reasoning, Morgan Kaufmann, 1987, pp. 145–152; also in this volume, pp.

    000–000.)

    Minsky, Marvin (1974). A Framework for Representing Knowledge, M.I.T.

    AI Memo 252.

    Shortliffe, Edward H. (1976). Computer-Based Medical Consultations: MYCIN,American Elsevier, New York, NY.

    ANSWERS TO QUESTIONS

    DISCUSSION OF THE PAPER

    QUESTION: You said the programs need common sense, but that’s like

    saying, If I could fly I wouldn’t have to pay Eastern Airliness $44 to haul me

    up here from Washington. So if the programs indeed need common sense,

    how do we go about it? Isn’t that the point of the argument?

    DR. MCCARTHY: I could have made this a defensive talk about artificial

    intelligence, but I chose to emphasize the problems that have been identified

    rather than the progress that has been made in solving them. Let me remind

    you that I have argued that the need for common sense is not a truism. Many

    useful things can be done without it, e.g. MYCIN and also chess programs.

    QUESTION: There seemed to be a strong element in your talk about common

    sense, and even humans developing it, emphasizing an experiential compo-

    nent — particularly when you were giving your example of dropping a glass

    of water. I’m wondering whether the development of these programs is going

    to take similar amounts of time. Are you going to have to have them go

    through the sets of experiences and be evaluated? Is there work going on in

    terms of speeding up the process or is it going to take 20 years for a program

    from the time you’ve put in its initial state to work up to where it has a

    decent amount of common sense?

    DR. MCCARTHY: Consider your 20 years. If anyone had known in 1963

    how to make a program learn from its experience to do what a human does

    after 20 years, they might have done it, and it might be pretty smart by now.

    Already in 1958 there had been work on programs that learn from experi-

    ence. However, all they could learn was to set optimal values of numerical

    parameters in the program, and they were quite limited in their ability to

    do that. Arthur Samuel’s checker program learned optimal values for its

    parameters, but the problem was that certain kinds of desired behavior did

    not correspond to any setting of the parameters, because it depended on the

    recognition of a certain kind of strategic situation. Thus the first prerequisite

    for a program to be able to learn something is that it be able to represent

    internally the desired modification of behavior. Simple changes in behavior

    must have simple representations. Turing’s universality theory convinces us

    that arbitrary behaviors can be represented, but they don’t tell us how to

    represent them in such a way that a small change in behavior is a small

    change in representation. Present methods of changing programs amount to

    education by brain surgery.

    QUESTION: I would ask you a question about programs needing common

    sense in a slightly different way, and I want to use the MYCIN program as

    an example.

    There are three actors there — the program, the physician, and the pa-

    tient. Taking as a criterion the safety of the patient, I submit that you need

    at least two of these three actors to have common sense.

    For example if (and sometimes this is the case) one only were sufficient,

    it would have to be the patient because if the program didn’t use common

    sense and the physician didn’t use common sense, the patient would have to

    have common sense and just leave. But usually, if the program had common

    sense built in and the physician had common sense but the patient didn’t, it

    really might not matter because the patient would do what he or she wants

    to do anyway.

    Let me take another possibility. If only the program has common sense

    and neither the physician nor the patient has common sense, then in the long

    run the program also will not use the common sense. What I want to say is

    that these issues of common sense must be looked at in this kind of frame of

    reference.

    DR. MCCARTHY: In the use of MYCIN, the physician is supposed to supply

    the common sense. The question is whether the program must also have

    common sense, and I would say that the answer is not clear in the MYCIN

    case. Purely computational programs don’t require common sense, and none

    of the present chess programs have any. On the other hand, it seems clear

    that many other kinds of programs require common sense to be useful at all.

    /@steam.stanford.edu:/u/ftp/jmc/someneed.tex: begun 1996 May 12, latexed 1996 May 12 at 1:24 p.m.