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.cb AN EXAMPLE FOR NATURAL LANGUAGE UNDERSTANDING
.cb AND THE AI PROBLEMS IT RAISES
.turn off "$";
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The following story from the %2New York Times%1 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.
%3The 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.%1
"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.
5. How did Mr. Hug get hurt? Probably when he hit the
bottom of the shaft.
6. Where is Mr. Hug's home? (A question whose answer
requires a literal understanding of only one sentence of the
stories.) 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.
8. Was Mr. Hug conscious after the robbers left? Yes, he
cried out and his cries were heard.
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 %2New York Times%1 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.
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 gradually by dividing the problem
into parts which can be attacked separately. Here are some of the
components:
1. 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 %2artificial
natural language%1 - 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 language 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.
2. 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 Moore (1976) has characterized them,
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".
3. 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" language and expect the answers in the "fact"
language, i.e. we avoid grammar problems in both understanding the
questions and in expressing the answers.
.SKIP TO COLUMN 1
NOTES ON AN "UNDERSTANDER"
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
∃ e p1 p2 g1 g2 e1 e2 ... . event(e) ∧ person(p1) ∧ name(p1)
= "John. J. Hug" ∧ g1 ⊂ Robbers ∧ ... etc.
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.
1. 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
α{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 problem. 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 happened 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
axiomatizing 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.
2. 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 Furniture and a function called
salesmen and to assert
Hug ε salesmen(Furniture).
This will probably work although the logical connection between the
abstract entity Furniture and concrete chairs and tables needs to be
worked out. It would be over-simplified to identify Furniture with
the set of furniture 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 resist a tendency to apply Occam's razor
prematurely. Perhaps we can identify the abstract Furniture 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
%2salesmen%1 could be the predicate %2furniture%1 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.
3. Occam's razor. 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 %2multiply
entities beyond necessity%1. This could be attributed to the fact
that the %2New York Times%1 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 %2New York Times%1.
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 sentences
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 exclude 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 excluded 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 account only
to exclude them as improbable. If one wants to introduce
probabilities, it might make more sense to assign a probability to
the correctness of the minimal completion of a %2New York Times%1
story based on its past record in finding the relevant facts of
robberies.
Many of the problems of minimal completions are discussed in
MINIMA[F76,JMC], and those results will hopefully be integrated into
a future draft of this note.
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 in to 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.
To recapitulate: The original set of predicate calculus
sentences can be generated from the story as one goes along. Each
sentence is generated approximately 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...
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