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C00018 00003	NOTES ON AN "UNDERSTANDER"
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\CAN EXAMPLE FOR NATURAL LANGUAGE UNDERSTANDING AND THE AI PROBLEMS IT RAISES


\J	The following story from  the \F1New York Times\F0 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  in the case  of the
story about Tad discussed earlier in the natural language seminar.
\F2The 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.\F0

	"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.)

	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 \F1New York Times\F0 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 the  story about  Tad,  the  question of what  was Tad's
middle name or what year  the story occurred in does not  necessarily
have an answer.

	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.  

	The  grammar  of  such  a  language   would  be  trivial  and
mathematical  in character.  There  would be an  "English" version of
the formalism 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 that  idea is well
enough defined to say definitely whether it meets the above criteria.

	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.

	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  the  "syntax free
language".

	 b. An "understander" that constructs the "facts" from a text
in the "syntax free language".

	 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.\.
NOTES ON AN "UNDERSTANDER"

\J	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
{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
\F1salesmen\F0  could be  the  predicate  \F1furniture\F0 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 \F1multiply
entities beyond necessity\F0.  This could be  attributed to the  fact
that the \F1New York Times\F0 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 \F1New York Times\F0.

	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 \F1New  York Times\F0
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 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 rest of
the sentence.\.