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AN EXAMPLE FOR NATURAL

LANGUAGE UNDERSTANDING AND THE

AI PROBLEMS IT RAISES

John McCarthy

Computer Science Department

Stanford University

Stanford, CA 94305

jmc@cs.stanford.edu

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

1 THE STORY

The following story from the New York Times is my candidate for a target

for a natural language understander. The story is about a real world event,

and therefore the intentions of the author are less relevant for answering

questions than for made up stories. The main goal of this discussion is to say

what a person who has understood the story knows about the event. This

seems to me to be preliminary to making programs that can understand.

“A 61-year old furniture salesman was pushed down the shaft of

a freight elevator yesterday in his downtown Brooklyn store by two

robbers while a third attempted to crush him with the elevator car

because they were dissatisfied with the $1,200 they had forced him to

give them.

The buffer springs at the bottom of the shaft prevented the car

from crushing the salesman, John J. Hug, after he was pushed from

the first floor to the basement. The car stopped about 12 inches above

him as he flattened himself at the bottom of the pit.

Mr. Hug was pinned in the shaft for about half an hour until his

cries attracted the attention of a porter. The store at 340 Livingston

Street is part of the Seaman’s Quality Furniture chain.

Mr. Hug was removed by members of the Police Emergency Squad

and taken to Long Island College Hospital. He was badly shaken, but

after being treated for scrapes of his left arm and for a spinal injury

was released and went home. He lives at 62-01 69th Lane, Maspeth,

Queens.

He has worked for seven years at the store, on the corner of Nevins Street,

and this was the fourth time he had been held up in the store. The last time

was about one year ago, when his right arm was slashed by a knife-wielding

robber.”

An intelligent person or program should be able to answer the following

questions based on the information in the story:

  1. Who was in the store when the events began? Probably Mr. Hug alone. although the robbers might have been waiting for him, but if so, this

    would have probably been stated. What did the porter say to the robbers?

    Nothing, because the robbers left before he came.

  2. Who was in the store during the attempt to kill Mr. Hug? Mr. Hug and the robbers.
  3. Who had the money at the end? The robbers.
  4. Is Mr. Hug alive today? Yes, unless something else has happened to him.

    shaft.

  5. How did Mr. Hug get hurt? Probably when he hit the bottom of the
  6. Where is Mr. Hug’s home? (A question whose answer requires a literal understanding of only one sentence of the story.) Does Mr. Hug live

    in Brooklyn? No, he lives in Queens.

  7. What are the names and addresses of the robbers? This information is not available.

    his cries were heard.

  8. Was Mr. Hug conscious after the robbers left? Yes, he cried out and
  9. What would have happened if Mr. Hug had not flattened himself at the bottom of the pit? What would have happened if there were no buffer

    springs? Mr. Hug would have been crushed.

    10. Did Mr. Hug want to be crushed? No.

    11. Did the robbers tell Mr. Hug their names? No.

    12. Were the robbers present when the porter came? No.

    13. Did Mr. Hug like the robbers, and did they like him?

    14. Why did the robbers leave without killing Mr. Hug? Perhaps, they

    thought they had killed him, and perhaps their anger was appeased by the

    actions they had performed, and perhaps they had taken all the time they

    dared, and perhaps something specific happened to frighten them away.

    15. What would have happened if Mr. Hug had tried to run away?

    Perhaps he would have succeeded, but more likely they would have injured

    or killed him since probably they had weapons, and there were three of them.

    16. What can Mr. Hug do to avoid this in the future? No solution is

    entirely satisfactory. He could carry a gun or he could quit or he could get

    his employers to install an alarm system or maybe he will be lucky.

    17. Did Mr. Hug know he was going to be robbed? Does he know that

    he was robbed?

    18. Was Mr. Hug’s right arm slashed before his left arm was scratched?

    Yes, because the former was a year ago.

    19. How did the robber try to crush him with the car? By pressing the

    buttons or operating the control lever to make the car go to the bottom of

    the shaft.

    20. Why did Mr. Hug yell from the bottom of the elevator shaft? So as

    to attract the attention of someone who would rescue him.

    21. How long did the events take? More than half an hour but less than a

    day. Most of the time was spent by Mr. Hug filling out forms in the hospital.

    22. What crimes were committed? This question has the advantage that

    it is one that is normally answered on the basis of such a story, since the police

    report of the incident was probably the basis of the New York Times story.

    Robbery, possibly assault with a deadly weapon, and attempted murder are

    the more obvious crimes. One might specifically challenge natural language

    systems to answer this question.

    The above list of questions is rather random. I doubt that it covers all

    facets of understanding the story. It would be worthwhile to try to make up

    a list of questions that does cover substantially all aspects of the story in

    order to get as complete as possible an intuitive idea of what capabilities are

    involved in understanding such a story.

    Note that the story is about a real event so that such a question as what

    does the “J” in “John J. Hug” stand for has an answer.

    In a made-up

    story, questions about middle names or what year the story occurred in do

    not necessarily have an answer, and an intelligent person or program would

    know that too.

    2 ARTIFICIAL NATURAL LANGUAGE

    I think that artificial intelligence is not very close to being able to understand

    such stories in a genuine way. Therefore, I would like to sneak up on it grad-

    ually by dividing the problem into parts which can be attacked separately.

    Here are some of the components:

  10. A formalism capable of expressing the assertions of the sentences free from dependence on the grammar of the English language. A good test for

    such a formalism would be to produce a program for translating from the

    formalism into any of several natural languages. More weakly, it should be

    as easy for a human to translate from the formalism into a natural language

    as to translate from one known natural language to another. Let’s call this

    formalism an artificial natural language—ANL for short.

    The grammar of ANL should be trivial and mathematical in character.

    There would be an “English” version in which English words were used as

    identifiers, but there would still have to be a glossary that gives the precise

    meaning of the identifiers. There would also be a German and a Japanese

    version. The translation from the English version to the German or Japanese

    version would be a simple substitution for identifiers, and a German or

    Japanese who had learned the grammar could then translate into his lan-

    guage with the aid of the German or Japanese glossary.

    This idea has some resemblance to the idea of “deep structure,” but I

    have some doubts about whether either idea is well enough defined to say.

  11. A data structure for expressing the facts (apart from expressing the sentences). In such a data structure, it would be definite which robber pushed

    Mr. Hug first, and what the robbers said even though it is not stated in the

    story. Clearly some compromise is necessary here, since the data structure

    need not be able to express positions and velocities of molecules. Like the

    PLANNER languages, as Robert Moore has characterized them in his 1976

    MIT Master’s thesis, the descriptions would contain no disjunctions, and

    might be a collection of relations with constants as arguments where every

    relation not asserted (in a certain class) is automatically denied.

    Alternatively, the basis of this data structure might be various networks of

    nodes described by sentences in the predicate calculus. Some of the sentences

    would assert that certain programs applied to the data structures would

    answer certain questions. When such sentences existed, reasoning would

    include the operation of the programs.

    In this way, we would expect to

    avoid the extreme prolixity that arises when we attempt to do even simple

    calculations by pure predicate calculus deduction.

    The test of success for the “data structure” would be that a human could

    readily formally deduce the answers to the above questions using a proof

    checker. Most of the proof-checker would be straightforward, but there is a

    major problem concerned with when it is possible to “jump to a conclusion.”

  12. I see each of the following problems as a difficult AI problem: a. A “parser” that takes English into ANL.

    b. An “understander” that constructs the “facts” from a text in the ANL.

    c. Expression of the “general information” about the world that could

    allow getting the answers to the questions by formal reasoning from the

    “facts” and the “general information.” The “general information” would

    also contain non-sentence data structures and procedures, but the sentences

    would tell what goals can be achieved by running the procedures. In this

    way, we would get the best of the sentential and procedural representations

    of knowledge.

    d. A “problem solver” that could answer the above questions on the basis

    of the “facts.” We imagine the questions to be expressed in the “fact” lan-

    guage and expect the answers in the “fact” language, i.e. we avoid grammar

    problems in both understanding the questions and in expressing the answers.

    3 THE USE OF LOGICAL FORMULAS

    When my understander has digested the story of Mr. Hug, it will have added

    one or more predicate calculus sentences to its data base. One sentence will

    do if it has the form

    ∃ep1p2g1g2e1e2 . . . ( event(e)person(p1)

    name(p1) =(cid:48)(cid:48) J ohnJ.Hug (cid:48)(cid:48) ∧ g1 ⊂ Robbers ∧ . . .).

    In this form, all the entities involved in expressing the facts of the story

    are existentially quantified variables. The only constants in the formula

    would have been present in the system previously. However, it is probably

    better to use a collection of sentences introducing a collection of individual

    constants.

    In this case, there will be 20 or so new individual constants

    representing people, groups of people, the main event and its sub-events,

    places, organizations, etc.

    In representing the robbers, the system has a choice of representing them

    by three individual constants, R1, R2, and R3 or by using a single symbol G1

    to represent the group of robbers. A good system will probably use both. If

    the number of robbers were not specified, we would have to use a constant for

    the group. We have to identify the robber who operated the elevator while

    the others pushed Mr. Hug into the shaft. We shall call him R1. The other

    two are not discriminated in the story, but there is no harm in our calling

    them R2 and R3, even if there is no information to discriminate them. If

    there were 20 robbers, it would be a mistake to give them all individual

    names. Suppose it had further been stated that as the robbers left one of

    them threatened to return and kill Mr. Hug later but that it was not stated

    whether this robber was the same one who operated the elevator. We could

    designate this robber by R4, but we would not have sentences asserting that

    R4 was distinct from R1, R2 and R3; instead we would have a sentence

    asserting that R4 was one of these.

    It is tempting to identify the group

    of robbers with the set R1, R2, R3, but we may want to give the group

    some properties not enjoyed by the set of its members. Sentences with plural

    subjects express some rather tricky concepts. Thus, the group robbed the

    store, and this is not an assertion that each member robbed the store.

    The “members of the police emergency squad” presents a similar prob-

    lem. We don’t want to assert how many there were. In this connection, it

    may be worthwhile to distinguish between what happened and what we wish

    to assert about what happened. A language adequate to describe what hap-

    pened would not have to leave the number of policemen present vague and

    could give them each a name. In my old jargon, such a language would be

    metaphysically adequate though not epistemologically adequate. Devising a

    language that is only metaphysically adequate may be a worthwhile stage on

    the way to an epistemologically adequate system. By “devising a language”

    I mean defining a collection of predicate and constant symbols and axioma-

    tizing their general properties. This language should not be peculiar to the

    story of Mr. Hug, but we should not require that it be completely general in

    the present state of the science.

    It is not obvious how to express what we know when we are told that

    Mr. Hug is a furniture salesman. A direct approach is to define an abstract

    entity called F urniture and a function called salesmen and to assert Hug ∈

    salesmen(F urniture).

    This will probably work although the logical connection between the ab-

    stract entity F urniture and concrete chairs and tables needs to be worked

    out. It would be over-simplified to identify F urniture with the set of fur-

    niture in existence at that time, because one could be a salesman of space

    shuttles even though there don’t exist any yet. In my opinion, one should re-

    sist a tendency to apply Occam’s razor prematurely. Perhaps we can identify

    the abstract F urniture with the an extension of the predicate that tells us

    whether an object should be regarded as a piece of furniture, perhaps not. It

    does no harm to keep them separate for the time being. This case looks like

    an argument for using second order logic so that the argument of salesmen

    could be the predicate f urniture that tells whether an object is a piece of

    furniture. However, there are various techniques for getting the same result

    without the use of second order logic.

    4 THE NEED FOR NONMONOTONIC REA-SONING

    After reading the story, one is prepared to answer negatively the question of

    whether there was someone else besides Mr. Hug and the robbers present.

    However, sentences describing such another person could be added to the

    story without contradiction. Our basis for the negative answer is that we

    can construct a model of the facts stated in the story without such a person,

    and we are applying Occam’s razor in order to not multiply entities beyond

    necessity. This could be attributed to the fact that the New York Times tells

    the whole story when it can, but I think that by putting Occam’s razor into

    the system, we can get this result without having to formalize the New York

    Times.

    This suggests introducing the notion of the minimal completion of a story

    expressed in the predicate calculus. The minimal completion of the story is

    also a set of sentences in the predicate calculus, but it contains sentences

    asserting things like “The set of people in the store while the robbers were

    trying to crush Mr. Hug consists of Mr. Hug and the robbers.” These sen-

    tences are to be obtained from the original set by the application of a process

    formalizing Occam’s razor. This process works from a set of sentences and

    is not logical deduction although it might be accomplished by deduction in

    a meta-language that contained sentences about sets of sentences. As I have

    pointed out elsewhere, the process cannot be deduction, because it generates

    sentences that contradict sentences that are consistent with the original set

    of sentences.

    A number of the questions given in the previous section have answers

    that can be formally deduced from the minimal completion but not from the

    original list.

    It has been suggested that probabilistic reasoning should be used to ex-

    clude the presence of other people rather than Occam’s razor. The problem

    with this is that the number of additional entities that are not logically ex-

    cluded is limited only by one’s imagination so that it is not clear how one

    could construct a probabilistic model that took these possibilities into ac-

    count only to exclude them as improbable. If one wants to introduce proba-

    bilities, it might make more sense to assign a probability to the correctness of

    the minimal completion of a New York Times story based on its past record

    in finding the relevant facts of robberies.

    Another problem in constructing the completion is the isolation of the

    story from the rest of the world. The members of the Police Emergency

    Squad all have mothers (living or dead), but we don’t want to bring them

    into the completion—not to speak of bringing in more remote ancestors all

    of whom can be asserted to exist on the basis of axioms about people.

    5 CONCLUSION

    To recapitulate: The original set of predicate calculus sentences can be gen-

    erated from the story as one goes along. Each sentence is generated approx-

    imately from a sentence of the story with the aid of general knowledge and

    what has been generated from the previous sentences. (This will usually be

    the case if the story is well told although there are sometimes cases in which

    the correct way to express a sentence will depend on what follows - but this

    is not good writing.) The completion, however, will depend on the whole of

    the story.

    It might be interesting to consider what can be determined from a partial

    reading of the story—even stopping the reading in the middle of a sentence

    since what has appeared so far in a sentence often must be understood in

    order to even parse the re. . .

    /@steam.stanford.edu:/u/ftp/jmc/mrhug.tex: begun 1996 May 14, latexed 1996 May 14 at 9:31 a.m.