INDIRECT RESPONSES TO LOADED QUESTIONS* 
S. Jerrold Kaplan 
Department of Computer and Information Science 
University of Pensylvania 
Philadelphia, Pa. 19104 
Casual users of Natural Language (NL) 
computer systems are typically inexpert not 
only with regard to the technical details 
of the underlying programs, but often with 
regard to the structure and/or content of 
the domain of discourse. Consequently, NL 
systems must be designed to respond 
appropriately when they can detect a 
misconception on the part of the user. 
Several conventions exist in cooperative 
conversation that allow a speaker to 
indirectly encode their intentions and 
beliefs about the domain into their 
utterances, ("loading" the utterances), and 
allow (in fact, often require) a 
cooperative respondent to address those 
intentions and beliefs beyond a literal, 
direct response. To be effective, NL 
computer systems must do the same. The 
problem, then, is to provide practical 
computational tools which will determine 
both when an indirect response is required, 
and wh-~ that response should be, without 
requiring that large amounts of domain 
dependent world knowledge be encoded in 
special formalisms. 
This paper will take the position that 
distinguishing language driven inferences 
from domain driven inferences provides a 
framewor-~r a s--~ution to this problem in 
the Data Base (DB) query domain. An 
implemented query system (CO-OP) is 
described that uses this distinction to 
provide cooperative responses to DB 
queries, using only a standard (CODASYL) DB 
and a lexicon as sources of world 
knowledge. 
WHAT IS A LOADED QUESTION? 
A loaded question is one that 
indicates that the questioner presumes 
something to be true about the domain of 
discourse that is actually false. Question 
IA presumes lB. A cooperative speaker must 
* This work partially supported by NSF 
grant MCS 76-19466 
find IB assumable (i.e. not believe it to 
be false) in order to appropriately utter 
IA in a cooperative conversation, intend it 
literally, and expect a correct, direct 
response. 
IA. What day does John go to his 
weekly piano lesson? 
lB. John takes weekly piano lessons. 
IC. Tuesday. 
Similarly, 2A presumes 2B. 
2A. How many Bloody Marys did Bill 
down at the banquet? 
2B. Hard liquor was available at the 
banquet. 
2C. Zero. 
If the questioner believed 2B to be false, 
there would be no point in asking 2A - s/he 
would already know that the correct answer 
had to be "Zero." (2C). 
Both examples 1 and 2 can be explained 
by a convention of conversational 
cooperation: that a questioner should leave 
the respondent a choice of direct answers. 
That is, from the questioner's viewpoint 
upon asking a question, more than one 
direct answer must be possible. 
It follows, then, that if a question 
presupposes something about the domain of 
discourse, as IA does, that a questioner 
cannot felicitously utter the question and 
believe the presupposition to be false. 
This is a result of the fact that each 
direct answer to a question entails the 
question's presuppositions. (More 
formally, if question Q presupposes 
proposition P, then each question-direct 
answer pair (Q, Ai) entails P*.) Therefore, 
* This entailment condition is a necessary 
but not sufficient condition for 
presupposition. The concept of 
presupposition normally includes a 
condition that the negation of a 
202 
if a questioner believes a presupposition 
to be false, s/he leaves no options for a 
correct, direct response - violating the 
convention. Conversely, a respondent can 
infer in a cooperative conversation from 
the fact that a question has been asked, 
that the questioner finds it's 
presuppositions assumable. (In the terms 
of \[Keenan 71\], the logical presupposition 
is pragmatically presupposed.) 
Surprisingly, a more general semantic 
relationship exists that still allows a 
respondent to infer a questioner's beliefs. 
Consider the situation where a proposition 
is entailed by all but one of a question's 
direct answers. (Such a proposition will 
be called a presumption of the question.) 
By a similar argument, it follows that if a 
questioner believes that proposition to be 
false, s/he can infer the direct, correct 
answer to the question - it is the answer 
that does not entail the proposition. Once 
again, to ask such a question leaves the 
respondent no choice of (potentially) 
correct answers, violating the 
conversational convention. More 
importantly, upon being asked such a 
question, the respondent can infer what the 
questioner presume s about the context. 
Question 2A above presumes 2B, but 
does not presuppose it: 2B is not entailed 
by the direct answer 2C. Nonetheless, a 
questioner must find 2B assumable to 
felicitously ask 2A in a cooperative 
conversation - to do otherwise would 
violate the cooperative convention. 
Similarly, 3B below is a presumption but 
not a presupposition of 3A (it is not 
entailed by 3C). 
the failure of a presupposition renders a 
ques£ion infe1-\[cTtius because it leaves n~ 
Options for a direct response; 6he---~ur-6 
of a presimption renders a--question 
T~felTcihous because it leaves at most one 
option for a direct "response. (Note th-a-6 
the d-~fl-6Tti~n of presumption subsumes the 
definition of presupposition in this 
context.) 
CORRECTIVE INDIRECT RESPONSES 
In a cooperative conversation, if a 
respondent detects that a questioner 
incorrectly presumes something about the 
domain of discourse, s/he is required to 
correct that misimpression. A failure to 
do so will implicitly confirm the 
questioner's presumption. Consequently, it 
is not always the case that a correct, 
direct answer is the most cooperative 
response. When an incorrect presumption is 
detected, it is more cooperative to correct 
the presumption than to give a direct 
response. Such a response can be called a 
Corrective Indirect Response. For example, 
imagine question 4A uttered in a 
cooperative conversation when the 
respondent knows that no departments sell 
knives. 
4A. Which departments that 
knives also sell blade sharpeners? 
4B. None. 
4C. No deparments sell knives. 
sell 
3A. Did Sandy pass the prelims? 
3B. Sandy took the prelims. 
3C. No. 
If a questioner believes in the falsehood 
of a presupposition of a question, the 
question is inappropriate because s/he must 
believe that no direct answer can be 
correct; similarly, if a questioner 
believes in the falsehood of a presumption, 
the question is inappropriate because the 
questioner must know the answer to the 
question - it is the direct answer that 
does not entail the presumption. In short, 
proposition (in this case, the negation of 
the proposition expressed by a 
question-direct answer pair) should also 
entail its presuppositions. Consequently, 
the truth of a presupposition of a question 
is normally considered a prerequisite for 
an answer to be either true or false (for a 
more detailed discussion see \[Keenan 73\]). 
These subtleties of the concept of 
presupposition are irrelevant to this 
discussion, because false responses to 
questions are considered a-priori to be 
uncooperative. 
Although 4B is a direct, correct response 
in this context, it is less cooperative 
than 4C. This effect is explained by the 
fact that 4A presumes that some departments 
sell knives. To be cooperative, the 
respondent should correct the questioner's 
misimpression with an indirect response, 
informing the questioner that no 
departments sell knives (4C). (The direct, 
correct response 4B will reinforce the 
questioner's mistaken presumption in a 
cooperative conversation through it's 
failure to state otherwise.) A failure to 
produce corrective indirect responses is 
highly inappropriate in a cooperative 
conversation, and leads to "stonewalling" - 
the giving of very limited and precise 
responses that fail to address the larger 
goals and beliefs of the questioner. 
RELEVANCE TO DB QUERIES 
Most NL computer systems stonewall, 
because their designs erroneously assume 
that simply producing the correct, direct 
response to a query insures a cooperative 
response. (To a great extent, this 
assumption results from the view that NL 
203 
functions in this domain simply as a 
high-level query language.) Unfortunately, 
the domain of most realistic DB's are 
sufficiently complex that the user of a NL 
query facility (most likely a naive user) 
will frequently make incorrect presumptions 
in his or her queries. A NL system that is 
only capable of a direct-- response ~i~r 
necessarily produce meaningless responses 
to failed presuppositions, and stonewall on 
failed presumptions. Consider t~-e 
following hypothetical exchange with a 
typical NL query system: 
Q: Which students got a grade of F in 
CIS500 in Spring, '77? 
R: Nil. \[the empty set\] 
Q: Did anyone fail CIS500 in Spring, 
'77? 
R: No. 
Q: How many people passed CIS500 in 
Spring, '77? 
R: Zero. 
Q: Was CIS500 given in Spring '77? 
R: No. 
A cooperative NL query system should 
be able to detect that the initial query in 
the dialog incorrectly presumed that CIS500 
was offered in Spring, '77, and respond 
appropriately. This ability is essential 
to a NL system that will function in a 
practical environment, because the fact 
that NL is used in the interaction will 
imply to the users that the normal 
cooperative conventions followed in a human 
dialog will be observed by the machine. 
The CO-OP query system, described below, 
obeys a number of conversational 
conventions. 
While the definition of presumption 
given above may be of interest from a 
linguistic standpoint, it leaves much to be 
desired as a computational theory. 
Although it provides a descriptive model of 
certain aspects of conversational behavior, 
it does not provide an adequate basis for 
computing the presumptions of a given 
question in a reasonable way. By limiting 
the domain of application to the area of 
data retrieval, it is possible to show that 
the linguistic structure of questions 
encodes considerable information about the 
presumptions that the questioner has made. 
This structure can be exploited to compute 
a significant class of presumptions and 
provide appropriate corrective indirect 
responses. 
LANGUAGE DRIVEN VS. DOMAIN DRIVEN INFERENCE 
A long standing observation in AI 
research is that knowledge about the world 
- both procedural and declarative - is 
required in order to understand NL.* 
Consequently, a great deal of study has 
gone into determining just what type of 
knowledge is required, and how that 
knowledge is to be organized, accessed, and 
utilized. One practical difficulty with 
systems adopting this approach is that they 
require the encoding of large amounts of 
world knowledge to be properly tested, or 
even to function at all. It is not easy to 
determine if a particular failure of a 
system is due to an inadequacy in the 
formalism or simply an insufficient base of 
knowledge. Frequently, the collection and 
encoding of the appropriate knowledge is a 
painstaking and time consuming task, 
further hindering an effective evaluation. 
Most NL systems that follow this paradigm 
have a common property: they decompose the 
input into a suitable "meaning" 
representation, and rely on various 
deduction and/or reasoning mechanisms to 
provide the "intelligence" required to draw 
the necessary inferences. Inferences made 
in this way can be called domain** driven 
inferences, because they are motivated by 
the domain itself***. 
While domain driven inferences are 
surely essential to an understanding of NL 
(and will be a required part of any 
comprehensive cognitive model of human 
intelligence), they alone are not 
sufficient to produce a reasonable 
understanding of NL. Consider the 
following story: 
John is pretty crazy, and sometimes 
does strange things. Yesterday he went 
to Sardi's for dinner. He sat down, 
examined the menu, ordered a steak, and 
got up and left. 
For a NL system to infer that something 
unusual has happened in the story, it must 
distinguish the story from the events the 
story describes. A question answering 
system that would respond to "What did John 
eat?" with "A steak." cannot be said to 
understand the story. As a sequence of 
events, the passage contains nothing 
unusual - it simply omits details that can 
be filled in on the basis of common 
knowledge about restaurants. As a story, 
* For example, to understand the statement 
"I bought a briefcase yesterday, and today 
the handle broke off." it is necessary to 
know that briefcases typically have 
handles. 
** "Domain" here is meant to include 
general world knowledge, knowledge about 
the specific context, and inferencial rules 
of a general and/or specific nature about 
that knowledge. 
*** Of course, these inferences are 
actually made on the basis of descriptions 
of the domain (the internal meaning 
representation) and not the domain itself. 
What is to be evaluated in such systems is 
the sufficiency of that description in 
representing the domain. 
204 
however, it raises expectations that the 
events do not. Drawing the inference "John 
didn't eat the steak he ordered." requires 
knowledge about the language in addition to 
knowledge about the domain. Inferences 
that require language related knowledge can 
be called language driven inferences. 
Language driven inferences can be 
characterized as follows: they are based on 
the fact that a story, dialog, utterance, 
etc. is a description, and that the 
description itself ma Z exhibit useful 
properties not associated with the 
being desc--~bed.* These additional 
properties are used by speakers to encode 
essential information - a knowledge of 
language related conventions is required to 
understand NL. 
Language driven inferences have 
several useful properties in a 
computational framework. First, being 
based on general knowledge about the 
language, they do not require a large 
infusion of knowledge to operate in 
differing domains. As a result, they are 
somewhat more amenable to encoding in 
computer systems (requiring less 
programming effort), and tend to be more 
transportable to new domains. Second, they 
do not appear to be as subject to runaway 
inferencing, i.e. the inferencing is 
driven (and hence controlled) by the 
phrasing of the input. Third, they can 
often achieve results approximating that of 
domain driven inference techniques with 
substantially less computational machinery 
and execution time. 
As a simple example, consider the case 
of factive verbs. The sentence "John 
doesn't know that the Beatles broke up." 
carries the inference that the Beatles 
broke up. Treated as a domain driven 
inference, this result might typically be 
achieved as follows. The sentence could be 
parsed into a representation indicating 
John's lack of knowledge of the Beatles' 
breakup. Either immediately or at some 
suitable later time, a procedure might be 
invoked that encodes the knowledge "For 
someone to not know something, that 
something has to be the case." The 
inferencial procedures can then update the 
knowledge base accordingly. As a language 
driven inference, this inference can be 
regarded as a lexical property, i.e. that 
factive verbs presuppose their complements, 
and the complement immediately asserted, 
namely, that the Beatles broke up. (Note 
that this process cannot be reasonably said 
to "understand" the utterance, but achieves 
the same results.) Effectively, certain 
* In the story example, assumptions about 
the connectedness of the story and the 
uniformity of the level of description give 
rise to the inference that John didn't eat 
what he ordered. These assumptions are 
conventions in the language, and not 
properties of the situation being 
described. 
inference rules have been encoded directly 
into the lexical and syntactic structure of 
the language - facilitating the drawing of 
the inference without resorting to general 
reasoning processes. 
Another (simpler) type of language 
driven inferences are those that relate 
specifically to the structure of the 
discourse, and not to it's meaning. 
Consider the interpretation of anaphoric 
references such as "former", "latter", 
"vice versa", "respectively", etc. These 
words exploit the linear nature of language 
to convey their meaning. To infer the 
appropriate referents, a NL system must 
retain a sufficient amount of the structure 
of the text to determine the relative 
positions of potential referents. If the 
system "digests" a text into a non-linear 
representation (a common procedure), it is 
likely to lose the information required for 
understanding. 
The CO-OP system, described below, 
demonstrates that a language driven 
inference approach to computational systems 
can to a considerable extent produce 
appropriate NL behavior in practical 
domains without the overhead of a detailed 
and comprehensive world model. By limiting 
the domain of discourse to DB queries, the 
lexical and syntactic structure of the 
questions encodes sufficient information 
about the user's beliefs that ~ significant 
class of presumptions can be computed on a 
purely language driven--~si~. 
CO-OP: A COOPERATIVE QUERY SYSTEM 
The design and a pilot implementation 
of a NL query system (CO-OP) that provides 
cooperative responses and operates with a 
standard (CODASYL) DB system has been 
completed. In addition to producing direct 
answers, CO-OP is capable of producing a 
variety of indirect responses, including 
corrective indirect responses. The design 
methodology of the system is based on two 
observations: 
I) To a large extent, the inferencing 
required to detect the need for an 
indirect response and to select the 
appropriate one can be driven directly 
from the lexical and syntactic 
structure of the input question, and 
2) the information already encoded in 
standard ways in DB systems complements 
the language related knowledge 
sufficiently to produce appropriate 
conversational behavior without the 
need for separate "world knowledge" or 
"domain specific knowledge" modules. 
Consequently, the inferencing mechanisms 
required to produce the cooperative 
responses are domain transparent, in the 
205 
sense that they will produce appropriate 
behavior without modification from any 
suitable DB system. These mechanisms can 
therefore be transported to new DB's 
without modification. 
To illustrate this claim, a detailed 
description of the method by which 
corrective indirect responses are produced 
follows. 
THE META QUERY LANGUAGE 
Most DB queries can be viewed as 
requesting the selection of a subset (the 
response set) from a presented set of 
entities (this analysis follows \[Belnap 
76\]). Normally, the presented set is put 
through a series of restrictions, each of 
which produces a subset, until the response 
set is found. This view is formalized in 
the procedures that manipulate an 
intermediate representation of the query, 
called the Meta Query Language (MQL). 
The MQ\[. is a graph structure, where 
the nodes represent sets (in the the 
mathematical, not the DB sense) "presented" 
by the user, and the edges represent 
relations defined on those sets, derived 
from the lexical and syntactic structure of 
the input query. Conceptually, the direct 
response to a query is an N-place relation 
realized by obtaining the referent of the 
sets in the DB, and composing them 
according to the binary relations. Each 
composition will have the effect of 
selecting a subset of the current sets. 
The subsets will contain the elements that 
survive (participate) in the relation. 
(Actually, the responses are realized in a 
much more efficient fashion - this is 
simply a convenient view.) 
As an example, consider the query 
"Which students got Fs in Linguistics 
courses?" as diagrammed it: FIGURE i. 
GOT 
Meta Query Language representation of 
"Which students got FS in Linguistics 
courses ?" 
FIGURE 1 
This query would be parsed as presenting 4 
sets: "students", "Fs", "Linguistics", and 
"courses". (The sets "Linguistics" and 
"Fs" may appear counterintuitive, but 
should be viewed as singleton entities 
assumed by the user to exist somewhere in 
the DB.) The direct answer to the query 
would be a 4 place relation consisting of a 
column of students, grades (all Fs), 
departments (all Linguistics), and courses. 
For convenience, the columns containing 
singleton sets (grades and departments) 
would be removed, and the remaining list of 
students and associated courses presented 
to the user. 
Executing the query consists of 
passing the MQL representation of the query 
to an interpretive component that produces 
a query suitable for execution on a CODASYL 
DB using information associated for this 
purpose with the lexical items in the MQL. 
(The specific knowledge required to perform 
this translation is encoded purely at the 
lexical level: the only additional domain 
dependent knowledge required is access to 
the DB schema.) 
The MQL, by encoding some of the 
syntactic relationships present in the NL 
query, can hardly be said to capture the 
meaning of the question: it is merely a 
convenient representation formalizing 
certain linguistic characteristics of the 
query. The procedures that mainipulate 
this representation to generate inferences 
are based on observations of a general 
nature regarding these syntactic 
relationships. Consequently, these 
inferences are language driven inferences. 
COMPUTING CORRECTIVE INDIRECT RESPONSES 
The crucial observation required to 
produce a reasonable set of corrective 
indirect responses is that the MQL query 
presumes the non-emptiness of --~ 
connected -~bgraphs. Each c-onnected 
subgraph corresponds to a presumption the 
user has made about the domain of 
discourse. Consequently, should the 
initial query return a null response, the 
control structure can check the users 
presumptions by passing each connected 
subgraph to the interpretive component to 
check it's non-emptiness (notice that each 
subgraph itself constitutes a well formed 
query). Should a presumption prove false, 
an appropriate indirect response can be 
generated, rather than a meaningless or 
misleading direct response of "None." 
For example, in the query of FIGURE i, 
the subgraphs and their corresponding 
corrective indirect responses are (the 
numbers represent the sets the subgraphs 
consist of): 
i) "I don't know of any students." 
2) "I don't know of any Fs." 
3) "I don't know of any courses." 
4) "I don't know of any Linguistics." 
1,2) "I don't know of any students 
that got Fs. " 
2,3) "I don't know 6f any Fs in 
206 
courses." 
3,4) "I don't know of any Linguistics 
courses." 
1,2,3) "I don't know of any students 
that got Fs in courses." 
2,3,4) "I don't know of any Fs in 
linguistics courses." 
Suppose that there are no linguistics 
courses in the DB. Rather than presenting 
the direct, correct answer of "None.", the 
control structure will pass each connected 
subgraph in turn to be executed against the 
DB. It will discover that no linguistics 
courses exist in the DB, and so will 
respond with "I don't know of any 
linguistics courses." This corrective 
indirect response (and all responses 
generated through this method) will entail 
the direct answer, since they will entail 
the emptiness of the direct response set. 
Several aspects of this procedure are 
worthy of note. First, although the 
selection of the response is dependent on 
knowledge of the domain (as encoded in a 
very general sense in the DB system - not 
as separate theorems, structures, or 
programs), the computation of the 
presumptions is totally indepen-dent of 
domain s~ecifi~-" knowledge. Because these 
ihferences are driven solely by the parser 
output (MQL representation), the procedures 
that determine the presumptions (by 
computing subgraphs) require no knowledge 
of the DB. Consequently, producing 
corrective indirect responses from another 
DB, or even another DB system, requires no 
changes to the inferencing procedures. 
Secondly, the mechanism for selecting the 
indirect response is identical to the 
procedure for executing a query. No 
additional computational machinery need b-e 
invoked to select the appropriate indl--~ec--6 
~e \[ T--~d i~--, the computational 
overhead involved in checking and 
correcting the users presumptions is not 
incurred unless it has been determined that 
an indirect response may be required. 
Should the query succeed initially, no 
penalty in execution time will be paid f~-{ 
the ab~-\[i ty t__oo produce t-~e-- in~rect 
responses. In addition, the--~ly increase 
in space overhead is a small control 
program to produce the appropriate 
subgraphs (the linguistic generation of the 
indirect response is essentially free - it 
is a trivial addition to the paraphrase 
component already used in the parsing 
phase). 
Corrective indirect responses, 
produced in this fashion, are language 
driven inferences, because they are derived 
directly from the structure of the query as 
represented by the MQL. If the query were 
phrased differently, a different set of 
presumptions would be computed. (This is 
not a drawback, as it might seem at first - 
it insures that the response will be in 
terms that the user understands.) For these 
reasons, corrective indirect responses, 
made possible by a careful choice of 
representations and associated algorithms, 
are produced in a domain transparent 
fashion with minimal system overhead using 
knowledge already available in the DB. 
A SHORT SAMPLE SESSION 
This section contains an example 
interaction with the preliminary 
implementation of the CO-OP system as it 
now stands. An actual session has been 
abstracted and commented here. 
CO-OP is currently implemented on a 
DEC KI-10 under TOPS-10 mainly in 
UCI-RUTGERS-LISP, and runs in 95K words, 
including the SEED DB system. SEED is a 
commercially available CODASYL DB system, 
marketed by International Data Base 
Systems, Inc. \[Gerritsen 78\]. The DB is a 
live one, used by the National Center For 
Atmospheric Research (NCAR) in Boulder Co. 
to keep track of their computer users, 
projects, research sponsors, etc. The 
primary purpose of the DB is to track the 
use of NCAR facilities, for accounting and 
other uses. 
The user in the example is a 
hypothetical Computer Science department 
chairperson who has heard that the NCAR DB 
contains information about who is 
sponsoring research in various areas, and 
is interested in securing some research 
funds. S/he believes (incorrectly, it 
turns out) that funds are available to 
study mass storage. S/he begins by asking: 
Q: Who sponsors active projects in mass 
storage? 
R: I don't know of any projects in mass 
storage. 
The system is unaware of any projects in 
mass storage, and so explicitly corrects 
the users presumption that there are some. 
The user tries a more general question. 
Q: Who sponsors research in computer 
science? 
R: I don't know of anyone that sponsors 
research in computer science. 
Again, a corrective indirect response - but 
this time stating that no sponsors are 
known, rather than that no suitable 
projects are known. In addition to being 
able to generate corrective indirect 
responses, the system can choose the most 
appropriate one when more than one is 
applicable. This is done by observing that 
207 
the subgraphs of an MQL query are partially 
ordered according to a subgraph relation, 
and it is most appropriate to correct the 
"least" failing subgraphs with respect to 
this ordering. 
Getting nowhere, the chairperson now 
asks: 
list (sorted by contact), along with the 
projects they sponsor. Although the user 
has presumed that there is only one contact 
at Nasa Headquarters, the system provides 
the entire list, without objecting. This 
and other forms of sloppy reference are 
tolerated by the system. 
Q: What are the areas? 
and receives a list of the areas of 
interest (s/he knew that the projects at 
NCAR are grouped according to a 
predetermined list of areas of interest), 
identified by the area number and name. 
S/he observes that area #6 (data 
processing) and area #12 (numerical 
methods) look interesting, and follows up 
with: 
Q: Who sponsors projects in area 6? 
The response is a list of sponsor names 
with a supportive indirect component of the 
projects they sponsor in area 6, the name 
of the area (because only the number was 
supplied - the system doesn't currently 
remember that it just provided the area 
name to the user), and the project numbers 
of the sponsore, d projects. The user now 
decides that Nasa Headquarters looks the 
most promising, and so asks: 
Q: What is sponsored in numerical methods 
by Nasa Headquarters? 
After checking the DB, the system discovers 
that Nasa Headquarters doesn't sponsor 
anything in numerical methods. 
Additionally, it is unable to detect any 
failed presumptions on the part of the 
user. It therefore provides a negative 
response followed by a suggestive indirect 
response listing the projects that Nasa 
Headquarters sponsors in any area, in the 
hope that this will be helpful to the user. 
R: I don't know of anything in numerical 
methods that Nasa Headquarters sponsors. 
But you might be interested in anything 
that Nasa Headquarters sponsors... 
After perusing this list, the chairperson 
concludes that although the projects don't 
look very promising, s/he will get in touch 
with Nasa Headquarters. S/he asks: 
Q: Who is the contact at Nasa Headquarters? 
CONCLUSION 
The problem of producing apparently 
intelligent behavior from a NL system has 
traditionally been viewed in Artificial 
Intelligence as a problem of modelling 
human cognitive processes, or modelling 
knowledge about the real world. It has 
been demonstrated here that such approaches 
must include a pragmatic theory of the 
conventions and properties of the use of 
language, to function effectively. Domain 
driven inferences must be complemented by 
language driven inferences to appropriately 
process NL. Further, it has been argued 
that language driven inference mechanisms 
help to control the inference process, and 
can provide a more general and 
computationally attractive solutions to 
many problems previously thought to require 
domain driven inference. 
A descriptive theory of one type of 
cooperative indirect response to 
inappropriate questions has been presented, 
and extended to a prescriptive 
(computational) theory by restricting the 
domain of application to DB query systems. 
This theory has been implemented using 
language driven mechanisms in the design of 
CO-OP, a cooperative query system. The 
result is the generation of appropriate 
corrective indirect responses in a 
computationally efficient and domain 
transparent fashion. 
REFERENCES 
Austin, J.L., How To Do Things With Words, 
J.O. Urmson, Ed.,--Oxf~'{d University Press, 
N.Y. 1965. 
Belnap, N. D., and T. B. Steel, The 
of Questions and Answers, Yale 
Unlv rsT-ty Press, New Haven, Conn., 1976. 
Gerritsen, Rob, SEED Reference Manual, 
Version CO0 - B04 draft, Internationa--~a 
Base Systems, Inc., Philadelphia, Pa., 
19104, 1978. 
It turns out that there is a contact at 
Nasa Headquarters for each project 
sponsored, and so the system prints out the 
Grice, H. P., "Logic and Conversation", in 
Syntax and Semantics: Speech Acts, Vol. 3, 
(P. Cole and J. L. Morgan, Ed.), 
208 
Academic Press, N.Y., 1975. University Press, London, 1969. 
Harris, L. R., "Natural Language Data Base 
Query: Using the Data Base Itself as the 
Definition of World Knowledge and as an 
Extension of the Dictionary", Technical 
Report #TR 77-2, Mathematics Dept., 
Dartmouth College, Hanover, N.H., 1977. 
Weischedel, R. M., Computation of a Unique 
Class of Inferences: Presuppos--\[tTon and 
Entailment, Ph.D. dissertation, Dept. of 
Computer and Information Science, 
University of Pennsylvania, Philadelphia, 
Pa. 1975. 
Joshi, A. K., S. J. Kaplan, and R. M. 
Lee, "Approximate Responses from a Data 
Base Query System: An Application of 
Inferencing in Natural Language", in 
Proceedings of the 5th IJCAI, Vol. i, 
1977. 
Kaplan, S. Jerrold, "Cooperative Responses 
from a Natural Language Data Base Query 
System: Preliminary Report", Technical 
Report, Dept. of Computer and Information 
Science, Moore School, University of 
Pennsylvania, Philadelphia, Pa., 1977. 
Kaplan, S. J., and Joshi, A. K., 
"Cooperative Responses: An Application of 
Discourse Inference to Data Base Query 
Systems", to appear in proceedings of the 
Second Annual Conference of the Canadian 
Society for Computational Studies of 
Intelligence, Toronto, Ontario, July, 1978. 
Joshi, A. K., Kaplan, S. J., and Sag, I. 
A., "Cooperative Responses: Why Query 
Systems Stonewall", to appear in 
proceedings of the 7th International 
Conference on Computational Linguistics, 
Bergen, Norway, August, 1978. 
Keenan, E. L., "Two kinds of 
Presupposition in Natural Language", in 
Studies i_~n Linguistic Semantics, (C. J. 
Fillmore and D. T. Langendoen, Ed.), 
Holt, Rinehart, and Winston, N.Y., 1971. 
Keenan, E. L., and Hull, R. D., "The 
Logical Presuppositions of Questions and 
Answers", in Prasuppositionen in 
Philosophie und Lin@uistik, (Petofi an--d 
Frank, Ed.), Athenaum Verlag, Frankfurt, 
1973. 
Lee, Ronald M. "Informative Failure in 
Database Queries", Working Paper #77-11-05, 
Dept. of Decision Sciences, Wharton 
School, University of Pennsylvania, 1977. 
Lehnert, W., "Human and Computational 
Question Answering", in Cognitive Science, 
Vol. i, #i, 1977. 
Searle, J. R., Speech Acts, an Essay in 
th.__ee Philosophy of Language, Cambridge 
209 
