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.cb EXPERT SYSTEMS AND COMMON SENSE

.CB "John McCarthy, Stanford University"

.cb "Copyright 1983, John McCarthy"

	An %2expert system%1 is a computer program intended to embody the
knowledge and ability of an expert in a certain domain.  The ideas behind
them and many examples fill the artificial intelligence literature.
Their performance in their specialized domains are often
very impressive.  Nevertheless, hardly any of them have certain
%2common sense%1 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
 abilities and the problems that require them.

	Common sense
facts and methods are only very partially understood today, and
extending this understanding is the key problem facing artificial
intelligence.

	This isn't exactly a new point of view.
I have been advocating "Computer 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, perhaps
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.  Moreover, there
is substantial disagreement about what common sense abilities are
and how to program them in computers.

	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 laboratory tests, and some others.  It then recommends a certain
course of antibiotics.  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 infection, 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 different 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 %2production system%1.  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 %2Legionnella%1
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
experimentally, 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.
Programmer's 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 %2Cholerae vibrio%1
in his intestines.  MYCIN will cheerfully recommend two weeks of
tetracycline and nothing else.
Presumably 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.


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.

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 information 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 administration 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.  Moreover, 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 programs 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

	%2s = 1/2 g t%52%1

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.


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
corresponds 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 non-monotonic reasoning.

	Deductive reasoning in mathematical logic has the following
property - called monotonicity by analogy with similar mathematical
concepts.  Suppose we have a set of assumptions from which follow
certain conclusions.  Now suppose we add additional assumptions.
There may be some new conclusions, 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 non-monotonic
reasoning, which are just as formal as deduction and just as
susceptible to mathematical study and computer implementation.
Formalized non-monotonic reasoning turns out to give certain
rules of conjecture rather than rules of inference - their
conclusion are appropriate, but may be disconfirmed when
more facts are obtained.  One such method is %2circumscription%1,
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 %2closed world assumption%1
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.

	Non-monotonic reasoning also seems to be involved in human
communication.  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.
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References:

%3Davis, Randall; Buchanan, Bruce; and Shortliffe, Edward (1977)%1:
"Production Rules as a Representation for a Knowledge-Based Consultation
Program," %2Artificial Intelligence%1, Volume 8, Number 1, February.

%3McCarthy, John (1960)%1: "Programs with Common Sense," Proceedings of the
Teddington Conference on the Mechanization of Thought Processes, Her Majesty's
Stationery Office, London.

%3McCarthy, John and P.J. Hayes (1969)%1:  "Some Philosophical Problems from
the Standpoint of Artificial Intelligence", in D. Michie (ed), %2Machine
Intelligence 4%1, American Elsevier, New York, NY.

%3McCarthy, John (1980)%1: 
"Circumscription - A Form of Non-Monotonic Reasoning", %2Artificial
Intelligence%1, Volume 13, Numbers 1,2, April.
.<<aim 334, circum.new[s79,jmc]>>

%3Minsky, Marvin (1974)%1:
"A Framework for Representing Knowledge", %2M.I.T. AI Memo 252%1.

%3Shortliffe, Edward H. (1976)%1:
Computer-Based Medical Consultations: MYCIN, American Elsevier, New York, NY.