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PROGRAMS WITH COMMON SENSE
Computer Science Department
Stanford, CA 94305
Interesting work is being done in programming computers to solve problems
which require a high degree of intelligence in humans. However, certain
elementary verbal reasoning processes so simple that they can be carried
out by any non-feeble minded human have yet to be simulated by machine
This paper will discuss programs to manipulate in a suitable formal lan-
guage (most likely a part of the predicate calculus) common instrumental
statements. The basic program will draw immediate conclusions from a list
of premises. These conclusions will be either declarative or imperative sen-
tences. When an imperative sentence is deduced the program takes a cor-
responding action. These actions may include printing sentences, moving
sentences on lists, and reinitiating the basic deduction process on these lists.
Facilities will be provided for communication with humans in the system
via manual intervention and display devices connected to the computer.
The advice taker is a proposed program for solving problems by manip-
ulating sentences in formal languages. The main diﬀerence between it and
other programs or proposed programs for manipulating formal languages (the
Logic Theory Machine of Newell, Simon and Shaw and the Geometry Pro-
gram of Gelernter) is that in the previous programs the formal system was
the subject matter but the heuristics were all embodied in the program. In
this program the procedures will be described as much as possible in the
language itself and, in particular, the heuristics are all so described.
The main advantages we expect the advice taker to have is that its behav-
ior will be improvable merely by making statements to it, telling it about its
symbolic environment and what is wanted from it. To make these statements
will require little if any knowledge of the program or the previous knowledge
of the advice taker. One will be able to assume that the advice taker will
have available to it a fairly wide class of immediate logical consequences of
anything it is told and its previous knowledge. This property is expected to
have much in common with what makes us describe certain humans as hav-
ing common sense. We shall therefore say that a program has common sense
if it automatically deduces for itself a suﬃciently wide class of immediate
consequences of anything it is told and what it already knows.
The design of this system will be a joint project with Marvin Minsky, but
Minsky is not to be held responsible for the views expressed here.1
Before describing the advice taker in any detail, I would like to describe
more fully our motivation for proceeding in this direction. Our ultimate
objective is to make programs that learn from their experience as eﬀectively
as humans do. It may not be realized how far we are presently from this
objective. It is not hard to make machines learn from experience to make
simple changes in their behavior of a kind which has been anticipated by
the programmer. For example, Samuel has included in his checker program
facilities for improving the weights the machine assigns to various factors in
evaluating positions. He has also included a scheme whereby the machine
remembers games it has played previously and deviates from its previous
play when it ﬁnds a position which it previously lost. Suppose, however, that
we wanted an improvement in behavior corresponding, say, to the discovery
by the machine of the principle of the opposition in checkers. No present or
presently proposed schemes are capable of discovering phenomena as abstract
If one wants a machine to be able to discover an abstraction, it seems
most likely that the machine must be able to represent this abstraction in
11996: This was wishful thinking. Minsky’s approach to AI was quite diﬀerent.
some relatively simple way.
There is one known way of making a machine capable of learning arbi-
trary behavior; thus to anticipate every kind of behavior. This is to make it
possible for the machine to simulate arbitrary behaviors and try them out.
These behaviors may be represented either by nerve nets (Minsky 1956),
by Turing machines (McCarthy 1956), or by calculator programs (Friedberg
1958). The diﬃculty is two-fold. First, in any of these representations the
density of interesting behaviors is incredibly low. Second, and even more
important, small interesting changes in behavior expressed at a high level of
abstraction do not have simple representations. It is as though the human
genetic structure were represented by a set of blue-prints. Then a mutation
would usually result in a wart or a failure of parts to meet, or even an un-
grammatical blue-print which could not be translated into an animal at all.
It is very diﬃcult to see how the genetic representation scheme manages to
be general enough to represent the great variety of animals observed and yet
be such that so many interesting changes in the organism are represented by
small genetic changes. The problem of how such a representation controls the
development of a fertilized egg into a mature animal is even more diﬃcult.
In our opinion, a system which is to evolve intelligence of human order
should have at least the following features:
program in some general purpose programming language.
the fact that the eﬀect of a subroutine is not usually good or bad in
itself. Therefore, the mechanism that selects subroutines should have
concepts of interesting or powerful subroutine whose application may
be good under suitable conditions.
Of the 5 points mentioned above, our work concentrates mainly on the
second. We base ourselves on the idea that: In order for a program to be
capable of learning something it must ﬁrst be capable of being told it. In fact,
in the early versions we shall concentrate entirely on this point and attempt
to achieve a system which can be told to make a speciﬁc improvement in its
behavior with no more knowledge of its internal structure or previous knowl-
edge than is required in order to instruct a human. Once this is achieved, we
may be able to tell the advice taker how to learn from experience.
The main distinction between the way one programs a computer and
modiﬁes the program and the way one instructs a human or will instruct the
advice taker is this: A machine is instructed mainly in the form of a sequence
of imperative sentences; while a human is instructed mainly in declarative
sentences describing the situation in which action is required together with
a few imperatives that say what is wanted. We shall list the advantages of
of the two methods of instruction.
Advantages of Imperative Sentences
Advantages of Declarative Sentences
quences of what it is told and what it previously knew.
of the instructor.
The only way we know of expressing abstractions (such as the previous
example of the opposition in checkers) is in language. That is why we have
decided to program a system which reasons verbally.
2 The Construction of the Advice Taker
The advice taker system has the following main features:
is deﬁned and a term is an expression. A sequence of expressions is an ex-
pression. These expressions are represented in the machine by list structures
(Newell and Simon 1957).
ical system. The particular system chosen will depend on programming
considerations but will probably have a single rule of inference which will
combine substitution for variables with modus ponens. The purpose of the
combination is to avoid choking the machine with special cases of general
propositions already deduced.
deduction routine will simply write down all one-step consequences of the
premises. Later, this may be elaborated so that the routine will produce
some other conclusions which may be of interest. However, this routine
will not use semantic heuristics; i.e., heuristics which depend on the subject
matter under discussion.
The intelligence, if any, of the advice taker will not be embodied in the
immediate deduction routine. This intelligence will be embodied in the pro-
cedures which choose the lists of premises to which the immediate deduction
routine is to be applied. Of course, the program should never attempt to ap-
ply the immediate deduction routine simultaneously to the list of everything
it knows. This would make the deduction routine take too long.
represent objects. For our purposes, an entity is an object if we have some-
thing to say about it other than the things which may be deduced from the
form of its name. For example, to most people, the number 3812 is not an
object: they have nothing to say about it except what can be deduced from
its structure. On the other hand, to most Americans the number 1776 is an
object because they have ﬁled somewhere the fact that it represents the year
when the American Revolution started. In the advice taker each object has
a property list in which are listed the speciﬁc things we have to say about
it. Some things which can be deduced from the name of the object may be
included in the property list anyhow if the deduction was actually carried
out and was diﬃcult enough so that the system does not want to carry it out
Some of the conclusions have the form of imperative sentences. These are
obeyed. Included in the set of imperatives which may be obeyed is the routine
which deduces and obeys.
We shall illustrate the way the advice taker is supposed to act by means
of an example. Assume that I am seated at my desk at home and I wish to go
to the airport. My car is at my home also. The solution of the problem is to
walk to the car and drive the car to the airport. First, we shall give a formal
statement of the premises the advice taker uses to draw the conclusions. Then
we shall discuss the heuristics which cause the advice taker to assemble these
premises from the totality of facts it has available. The premises come in
groups, and we shall explain the interpretation of each group.
at(x, y)” is a formalization of
“xisaty”. Under this heading we have the premises
We shall need the fact that the relation“at” is transitive which might be
written directly as
at(y, z) →
or alternatively we might instead use the more abstract premises
transitive(u) → (
u(y, z) →
from which (6) can be deduced.
can(go(y, z, walking))
at(I, car) →
can(go(y, z, driving))
There are also two speciﬁc facts
did(go(x, y, z))→
did(y) → z) →
canachult(x, y, z)
The predicate “
canachult(x, y, z)” means that in a situation to which x ap-
plies, the action y can be performed and ultimately brings about a situation
to which z applies. A sort of transitivity is described by
canachult(x, y, z),
canachult(z, u, v) →
canachult(x, prog(y, u), v).
prog(u, v) is the program of ﬁrst carrying out u and then v. (Some
kind of identiﬁcation of a single action u with the one step program
is obviously required, but the details of how this will ﬁt into the formalism
have not yet been worked out).
The ﬁnal premise is the one which causes action to be taken.
canachult(x, prog(y, z), w),
The argument the advice taker must produce in order to solve the problem
deduces the following propositions in more or less the following order:
can(go(desk, car, walking))
can(go(home, airport, driving))
did(go(desk, car, walking))→
did(go(home, airport, driving))→
canachult(at(I, desk), go(desk, car, walking), at(I, car))
canachult(at(I, car), go(home, airport, driving), at(I, airport))
prog(go(desk, car, walking), go(home, airport, driving))→
do(go(desk, car, walking))The deduction of the last proposition initiates action.
The above proposed reasoning raises two major questions of heuristic.
The ﬁrst is that of how the 17 premises are collected, and the second is
that of how the deduction proceeds once they are found. We cannot give
complete answers to either question in the present paper; they are obviously
not completely separate since some of the deductions might be made before
some of the premises are collected. Let us ﬁrst consider the question of where
the 17 premises came from.
First of all, we assert that except for the 14th premise
which sets the goal, and the 1st premise
at(I, desk), which we shall get from
a routine which answers the question “whereamI (cid:48)(cid:48), all the premises can rea-
sonably be expected to be speciﬁcally present in the memory of a machine
which has competence of human order in ﬁnding its way around. That is,
none of them are so speciﬁc to the problem at hand that assuming their pres-
ence in memory constitutes an anticipation of this particular problem or of a
class of problems narrower than those which any human can expect to have
previously solved. We must impose this requirement if we are to be able to
say that the advice taker exhibits common sense.
On the other hand, while we may reasonably assume that the premises
are in memory, we still have to describe how they are assembled into a
list by themselves to which the deduction routine may be applied. Tenta-
tively, we expect the advice taker to proceed as follows: initially, the sentence
want(at(I, airport))” is on a certain list L, called the main list, all by itself.
The program begins with an observation routine which looks at the main list
and puts certain statements about the contents of this list on a list called
“observations of the main list”. We shall not specify at present what all the
possible outputs of this observation routine are but merely say that in this
case it will observe that “the only statement on L has the form (cid:48)
(We write this out in English because we have not yet settled on a formalism
for representing statements of this kind). The “deduce and obey” routine
is then applied to the combination of the “observations of the main list”
list, and a list called the “standing orders list”. This list is rather small
and is never changed, or at least is only changed in major changes of the
advice taker. The contents of the “standing orders” list has not been worked
out, but what must be deduced is the extraction of certain statements from
property lists. Namely, the program ﬁrst looks at “
want(at(I, airport))” and
attempts to copy the statements on its property list. Let us assume that it
fails in this attempt because “
want(at(I, airport))” does not have the sta-
tus of an object and hence has no property list. (One might expect that if
the problem of going to the airport has arisen before, “
would be an object, but this might depend on whether there were routines
for generalizing previous experience that would allow something of general
use to be ﬁled under that heading). Next in order of increasing generality the
machine would see if anything were ﬁled under “
want(at(I, x))” which would
deal with the general problem of getting somewhere. One would expect that
premises (6), (or (7) and (8)), (9), (10), (13), would be so ﬁled. There would
also be the formula
want(at(I, x)) →
which would give us premise (1). There would also be a reference to the next
higher level of abstraction in the goal statement which would cause a look at
the property list of “
want(x)”. This would give us (15), (16), and (17).
We shall not try to follow the solution further except to remark that on
the property list of “
want(at(I, x))” there would be a rule that starts with
the premises “
at(I, y)” and “
want(I, x)” and has as conclusion a search for
the property list of “
go(y, x, z)”. This would presumably fail, and then there
would have to be heuristics that would initiate a search for a y such that
at(I, y)” and “
at(airport, y)”. This would be done by looking on the prop-
erty lists of the origin and the destination and working up. Then premise
(10) would be found which has as one of its premises
at(I, car). A repeti-
tion of the above would ﬁnd premise (9), which would complete the set of
premises since the other “at” premises would have been found as by-products
of previous searches.
We hope that the presence of the heuristic rules mentioned on the prop-
erty lists where we have put them will seem plausible to the reader. It should
be noticed that on the higher level of abstraction many of the statements are
of the stimulus-response form. One might conjecture that division in man
between conscious and unconscious thought occurs at the boundary between
stimulus-response heuristics which do not have to be reasoned about but only
obeyed, and the others which have to serve as premises in deductions.
We hope to formalize the heuristics in another paper before we start
programming the system.
Friedberg, R. (1958). A Learning Machine, Part I IBM Journal of Research
and Development 2, No. 1.
McCarthy, John (1956). The Inversion of Functions Deﬁned by Turing Ma-
chines, in Automata Studies, Annals of Mathematical Study No. 34, Prince-
ton, pp. 177–181.
Minsky, M.L. (1956). Heuristic Aspects of the Artiﬁcial Intellegence Prob-
lem. Lincoln Laboratory Report,pp.34–55.
Newell, A., Shaw, J. C. and Simon, H.A.(1957). Empirical Explorations of
the Logic Theory Machine. A case Study in Heuristic. Proceedings of the
Western Joint Computer Conference, published by the Institute of Radio
Engineers, New York, 1957, pp. 218–230.
4 DISCUSSION OF THE PAPER
PROF. Y. BAR-HILLEL: Dr. McCarthy’s paper belongs in the Journal of
Half-Baked Ideas, the creation of which was recently proposed by Dr. I. J.
Good. Dr. McCarthy will probably be the ﬁrst to admit this. Before he goes
on to bake his ideas fully, it might be well to give him some advice and raise
some objections. He himself mentions some possible objections, but I do not
think that he treats them with the full consideration they deserve; there are
others he does not mention.
For lack of time, I shall not go into the ﬁrst part of his paper, although
I think that it contains a lot of highly unclear philosophical, or pseudo-
philosophical assumptions. I shall rather spend my time in commenting on
the example he works out in his paper at some length. Before I start, let
me voice my protest against the general assumption of Dr. McCarthy —
slightly caricatured — that a machine, if only its program is speciﬁed with
a suﬃcient degree of carelessness, will be able to carry out satisfactory even
rather diﬃcult tasks.
Consider the assumption that the relation he designates by at is transitive
(page 7). However, since he takes both “
at(I, desk)” and “
as premises, I presume – though this is never made quite clear – that at means
something like being-a-physical-part or in-the-immediate-spatial-neighborhood-of. But then the relation is clearly not transitive. If A is in the immediate
spatial neighborhood of B and B in the immediate spatial neighborhood of C
then A need not be in the immediate spatial neighborhood of C. Otherwise,
everything would turn out to be in the immediate spatial neighborhood of
everything, which is surely not Dr. McCarthy’s intention. Of course, start-
ing from false premises, one can still arrive at right conclusions. We do such
things quite often, and a machine could do it. But it would probably be bad
advice to allow a machine to do such things consistently.
Many of the other 23 steps in Dr. McCarthy’s argument are equally or
more questionable, but I don’t think we should spend our time showing this in
detail. My major question is the following: On page 9 McCarthy states that
a machine which has a competence of human order in ﬁnding its way around
will have almost all the premises of the argument stored in its memory. I am
at a complete loss to understand the point of this remark. If Dr. McCarthy
wants to say no more than that a machine, in order to behave like a human
being, must have the knowledge of a human being, then this is surely not a
very important remark to make. But if not, what was the intention of this
The decisive question how a machine, even assuming that it will have
somehow countless millions of facts stored in its memory, will be able to pick
out those facts which will serve as premises for its deduction is promised to
receive its treatment in another paper, which is quite right for a half-baked
It sounds rather incredible that the machine could have arrived at its
conclusion — which, in plain English, is “Walk from your desk to your car!”
— by sound deduction. This conclusion surely could not possibly follow from
the premise in any serious sense. Might it not be occasionally cheaper to call
a taxi and have it take you over to the airport? Couldn’t you decide to cancel
your ﬂight or to do a hundred other things? I don’t think it would be wise
to develop a programming language so powerful as to make a machine arrive
at the conclusion Dr. McCarthy apparently intends it to make.
Let me also point out that in the example the time factor has never been
mentioned, probably for the sake of simplicity. But clearly this factor is here
so important that it could not possibly be disregarded without distorting the
whole argument. Does not the solution depend, among thousands of other
things, also upon the time of my being at my desk, the time at which I have
to be at the airport, the distance from the airport, the speed of my car, etc.
To make the argument deductively sound, its complexity will have to
be increased by many orders of magnitude. So long as this is not realized,
any discussions of machines able to perform the deductive — and inductive!
— operations necessary for treating problems of the kind brought forward
by Dr. McCarthy is totally pointless. The gap between Dr. McCarthy’s
general programme (with which I have little quarrel, after discounting its
“philosophical” features) and its execution even in such a simple case as the
one discussed seems to me so enormous that much more has to be done to
persuade me that even the ﬁrst step in bridging this gap has already been
DR. O. G. SELFRIDGE: I have a question which I think applies to this. It
seems to me in much of that work, the old absolutist Prof. Bar-Hillel has
really put his ﬁnger on something; he is really worried about the deduction
actually made. He seemed really to worry that that system is not consistent,
and he made a remark that conclusions should not be drawn from false
In my experience those are the only conclusions that have ever
been drawn. I have never yet heard of someone drawing correct conclusions
from correct premises. I mean this seriously. This, I think is Dr. Minsky’s
point this morning. What this leads to is that the notion of deductive logic
being something sitting there sacred which you can borrow for particularly
sacred uses and producing inviolable results is a lot of nonsense. Deductive
logic is inferrred as much as anything else. Most women have never inferred
it, but they get along pretty well, marrying happy husbands, raising happy
children, without ever using deductive logic at all. My feeling is that my
criticism of Dr. McCarthy is the other way. He assumes deductive logic,
whereas in fact that is something to be concocted.
This is another important point which I think Prof. Bar-Hillel ignores in
this, the criticism of the programme should not be as to whether it is logically
consistent, but only will he be able to wave it around saying “this in fact
works the way I want it”. Dr. McCarthy would be the ﬁrst to admit that his
proramme is not now working, so it has to be changed. Then can you make
the changes in the programme to make it work? That has nothing to do with
logic. Can he amend it in such a way that it includes the logic as well as the
little details of the programme? Can he manage in such a way that it works
the way he does? He said at the begining of his talk that when he makes an
arbitrary change in the programme it will not work usually, evidence, to me
at least, that small changes in his programme will not obviously make the
programme work and might even improve it. His next point is whether he
can make small changes that in fact make it work. That is what we do not
PROF. Y. BAR-HILLEL: May I ask whether you could thrash this out with
Dr. McCarthy? It was my impression that Dr. McCarthy’s advice taker
was meant to be able, among other things, to arrive at a certain conclusion
from appropriate premises by faultless deductive reasoning. If this is still his
programme, then I think your defence is totally beside the point.
DR. O. G. SELFRIDGE: I am not defending his programme, I am only
DR. J. McCARTHY: Are you using the word ‘programme’ in the technical
sense of a bunch of cards or in the sense of a project that you get money for?
PROF. Y. BAR-HILLEL: When I uttered my doubts that a machine working
under the programme outlined by Dr. McCarthy would be able to do what
he expects it to do, I was using ‘programme’ in the technical sense.
DR. O. G. SELFRIDGE: In that case your criticisms are not so much philo-
sophical as technical.
PROF. Y. BAR-HILLEL: They are purely technical. I said that I shall not
make any philosophical criticisms, for lack of time.
DR. O. G. SELFRIDGE: A technical objection does not make ideas half-
PROF. Y. BAR-HILLEL: A deductive argument, where you have ﬁrst to
ﬁnd out what are the relevant premises, is something which many humans
are not always able to carry out successfully. I do not see the slightest reason
to believe that at present machines should be able to perform things that
humans ﬁnd trouble in doing. I do not think there could possibly exist a
programme which would, given any problem, divide all facts in the universe
into those which are and those which are not relevant for that problem.
Developing such a programme seems to me by 1010 orders of magnitude
more diﬃcult than, say, the Newell-Simon problem of developing a heuristic
for deduction in the propositional calculus. This cavalier way of jumping
over orders of magnitude only tends to becloud the issue and throw doubt
on ways of thinking for which I have a great deal of respect. By developing
a powerful programme language you may have paved the way for the ﬁrst
step in solving problems of the kind treated in your example, but the claim
of being well on the way towards their solution is a gross exaggeration. This
was the major point of my objections.
DR. MCCARTHY (in reply): Prof. Bar-Hillel has correctly observed that
my paper is based on unstated philosophical assumptions although what
he means by “pseudo-philosophical” is unclear. Whenever we program a
computer to learn from experience we build into the program a sort of epis-
temology. It might be argued that this epistemology should be made explicit
before one writes the programme, but epistemology is in a foggier state than
computer programming even in the present half-baked state of the latter.
I hope that once we have succeeded in making computer programs reason
about the world, we will be able to reformulate epistemology as a branch of
applied mathematics no more mysterious or controversial than physics.
On re-reading my paper I can’t see how Prof. Bar-Hillel could see in it a
proposal to specify a computer program carelessly. Since other people have
proposed this as a device for achieving “creativity”, I can only conclude that
he has some other paper in mind.
In his criticism of my use of the symbol “at”, Prof. Bar-Hillel seems to
have misunderstood the intent of the example. First of all, I was not trying
to formalize the sentence form, A is at B, as it is used in English. “at” merely
was intended to serve as a convenient mnemonic for the relation between a
place and a sub-place. Second, I was not proposing a practical problem for
the program to solve but rather an example intended to allow us to think
about the kinds of reasoning involved and how a machine may be made to
Prof. Bar-Hillel’s major point concerns my statement that the premises
listed could be assumed to be in memory. The intention of this statement is
to explain why I have not included formalizations of statements like, “it is
possible to drive from my home to the airport” among my premises. If there
were n known places in the county there would be
such sentences and, since we are quite sure that we do not have each of them
in our memories, it would be cheating to allow the machine to start with
The rest of Prof. Bar-Hillel’s criticisms concern ways in which the model
mentioned does not reﬂect the real world; I have already explained that
this was not my intention. He is certainly right that the complexity of the
model will have to be increased for it to deal with practical problems. What
we disagree on is my contention that the conceptual diﬃculties arise at the
present level of complexity and that solving them will allow us to increase
the complexity of the model easily.
With regard to the discussion between Prof. Bar-Hillel and Oliver Self-
ridge — the logic is intended to be faultless although its premises cannot be
guaranteed. The intended conclusion is “
do(go(desk, car, walking))”—not,
of course, “
at(I, airport)”. The model oversimpliﬁes but is not intended to
oversimplify to the extent of allowing one to deduce one’s way to the airport.
n(n − 1)