WOULD I LIE TO YOU? 
MODELLING MISREPRESENTATION AND CONTEXT IN DIALOGUE 
Carl Gutwin 
Alberta Research Council 1 
6815 8th Street N. E. 
Calgary, Alberta T2E 7H7, Canada 
Internet: gutwin@ skyler.arc.ab.ca 
Gordon McCalla 
ARIES Laboratory, University of Saskatchewan 2 
Saskatoon, Saskatchewan S7N 0W0, Canada 
ABSTRACT 
In this paper we discuss a mechanism for 
modifying context in a tutorial dialogue. The context 
mechanism imposes a pedagogically motivated 
misrepresentation (PMM) on a dialogue to achieve 
instructional goals. In the paper, we outline several 
types of PMMs and detail a particular PMM in a 
sample dialogue situation. While the notion of 
PMMs are specifically oriented towards tutorial 
dialogue, misrepresentation has interesting 
implications for context in dialogue situations 
generally, and also suggests that Grice's maxim of 
quality needs to be modified. 
1. INTRODUCTION 
Most of the time, truth is a wonderful thing. 
However, this research studies situations where not 
saying what you believe to be the truth can be the 
best course of action. Intentional misrepresentation 
of a speaker's knowledge appears to be a common and 
highly pragmatic process used in many different kinds 
of dialogue, especially tutorial dialogue. 
We use imperfect or incomplete representations in 
response to constraints and demands imposed by the 
situation: for example, many models of the real 
world are extremely complex, and misrepresentations 
are often used as useful, comprehensible 
approximations of complicated systems. People use 
idealized Newtonian mechanics, the wave (or particle) 
theory of light, and rules of default reasoning stating 
that birds fly, penguins are birds, and penguins don't 
fly. Some systems which cannot be simplified are 
purposefully ignored: for example, higher order 
........................................................................ 
1 This research was completed while C. Gutwin was a 
graduate student at the University of Saskatchewan. All 
correspondence should be sent to the first author. 
2 Visiting scientist, Learning Research & Development 
Centre, University of Pittsburgh, 1991-92 
differential equations are left out of engineering 
classes because of their complexity. Simplified and 
imperfect representations are often found in tutoring 
discourse. 
Misrepresentation as a pedagogic strategy holds 
promise for extending the capabilities of intelligent 
tutoring systems (ITSs), but the concept also affects 
computational dialogue research: it builds on the 
idea of discourse focus and context, extends work on 
adapting to the user with multiple representations of 
knowledge, and challenges Grice's maxims of 
conversation. 
2. MOTIVATION AND BACKGROUND 
Misrepresentations are alterations to a perceived 
reality. When they have sincere pedagogic purposes, 
we name them Pedagogically Motivated 
Misrepresentations, or PMMs. PMMs can reduce the 
complexity of the dialogue and of the concepts to be 
learned, provide focus in a busy environment, or 
facilitate the communication of essential knowledge. 
PMMs share themes with research into 
computational dialogue and ITS. PMMs are 
intimately connected to ideas of instructional and 
dialogue focus, the latter of which was explored by 
Grosz \[1977\], who stated that task-oriented dialogue 
could be organized into focus spaces, each containing 
a subset of the dialogue's purposes and entities. The 
collection of focus spaces created by the changing 
dynamics of a dialogue could be gathered together into 
a focusing structure which assisted in interpreting new 
utterances. 
Adaptation to the hearer is also a concern in 
dialogue research: beliefs about the hearer or about the 
situation can be used to vary the structure, 
complexity, and language of discourse to optimally 
suit the hearer. Several projects (e.g. \[McKeown et al 
1985\], \[Moore & Swartout 1989\], \[Paris 1989\]) have 
152 
looked at adapting the level or tenor of explanations 
to a user's needs. Paris's \[1989\] TAILOR system 
varies its output (descriptions of complex devices) 
depending upon the hearer's expertise. 
Another concern in both dialogue research and ITS 
research is multiple representations of domain 
knowledge. TAILOR, for example, uses two different 
models of each device to construct its explanations. 
Tutoring systems like SMITHTOWN \[Shute and 
Bonar 1986\] and MHO \[Lesgold et al. 1987\] organize 
different representations around distinct pedagogic 
goals; in the domain of electrical circuits, QUEST 
\[Frederiksen & White 1988\] provides progressively 
more sophisticated representations, from a simple 
qualitative model to quantitative circuit theory. 
Lastly, any discussion of misrepresentation in 
dialogue is bound to reflect on Grice's first maxim of 
quality, "do not say that which you believe to be 
false." The conversational maxims of H. Paul Grice 
\[1977\] are a well-known set of observations about 
human discourse frequently used in computational 
dialogue research (for example \[Joshi et al 1984\], 
\[Moore and Paris 1989\], \[Reichman 1985\]). 
However, people sometimes accept the truth of 
Grice's maxims too easily. A close examination 
reveals difficulties with a literal interpretation of the 
first maxim of quality. While this maxim seems a 
reasonable rule to use in dialogue, examination of 
human discourse shows many instances where 
uttering falsehoods is legitimate behaviour. For 
example, in some first year computer science courses, 
students are told that a semicolon is the terminator of 
a Pascal statement. This utterance misrepresents 
reality (a semicolon actually separates statements), 
but the underlying purpose is sincere: the 
misrepresentation allows students to begin 
programming without forcing them to learn about 
syntax charts, parsing algorithms, or recursive 
definitions. Grice's maxims have avoided major 
criticism by the computational dialogue community, 
and the maxims have been successfully used in 
limited domains to help dialogue systems interact 
with their users. Realizing that misrepresentations 
often occur in tutorial discourse, however, provides us 
with a context for investigating limits to the Gricean 
approach. 
3. OVERVIEW OF PEDAGOGICALLY 
MOTIVATED MISREPRESENTATIONS 
We have identified and characterized several types 
of PMM that can occur in tutorial discourse. We 
define each type as a computational structure that, 
when invoked, alters the dialogue system's own 
reality and hence the student's perception of reality, 
for sincere pedagogic purposes. There are five 
essential computational characteristics governing the 
use of PMMs: preconditions, applicability 
conditions, removal conditions, revelation conditions, 
and effects. 
These conditions are predicates matched against 
information in the dialogue system's essential data 
structures: a domain knowledge representation (in 
this system, a granularity hierarchy after \[Greet and 
McCalla 1989\], as shown in Figure 1); a model of the 
student; and an instructional plan (in this system, a 
simplified version of Brecht's (1990) content planner, 
from which a sample partial plan is shown in Figure 
2). Each step in the instructional plan provides a 
teaching operator (such as prepare-to-teach) and a 
concept from the knowledge base which becomes the 
focus of the instructional interaction. 
I Major Programming Concept I 
Figure 1. A fragment of the domain representation 
In this implementation, PMMs act by 
manipulating the dialogue system's blackboard-based 
internal communication. An active PMM intercepts 
relevant messages before the knowledge base can 
receive them, then returns misrepresented information 
instead of the "true" information to the blackboard. 
153 
'UT ~' (conditional"-~ COI~ 
" STUDL~IT 1 ~'~ ~STUDEI~ r" KNOWS KI~WS ' 
(conol)hal ~¢~nditional expres ~xpressions) , 
Figure 2. A partial content plan from Brecht's \[1990\] 
planner. 
The first step in using a misrepresentation 
involves the PMM's preconditions and applicability 
conditions. Preconditions are definitional constraints 
characterizing situations in which a particular PMM 
is conceivable. Applicability conditions actually 
determine the suitability of a PMM to a situation. 
Each applicability condition examines one element of 
the current instructional context, from the student 
model, the domain representation, or the instructional 
plan. The individual conditions are combined to 
determine a final "score" for the PMM, using a 
calculus akin to MYCIN's certainty factors 
(\[Shortliffe 1976\]). For example, one applicability 
condition states that less student knowledge about a 
domain concept can provide evidence for the PMM's 
greater applicability, and more knowledge implies less 
applicability. 
A PMM's removal conditions provide a facility for 
determining when the misrepresentation is no longer 
useful and may be removed. However, a dialogue 
system also needs to know when a PMM is not 
working well; after all, there are certain dangers 
associated with the use of misrepresentations. For 
example, a student may realize the discrepancy 
between the altered environment and reality. These 
situations are monitored by a PMM's revelation 
conditions, guiding the system in cases where it must 
be ready to abandon the misrepresentation and reveal 
the misrepresentation. 
If preconditions and applicability conditions are 
satisfied, a PMM's procedural effects can be applied to 
the domain representation, implementing the 
'alternative reality' presented to the student through 
the dialogue. 
The way in which the student's perceived 
environment is altered and restored plays a crucial part 
in a misrepresentation's success. The dialogue actions 
which accomplish these changes compose two unique 
subdialogues. An alteration subdialogue must make a 
smooth transition to the altered environment; a 
restoration subdialogue has the opposite effect: it 
must restore the "real" environment, knot all the 
loose ends created by the misrepresentation, and help 
the student transfer knowledge from the 
misrepresented environment to the real environment. 
Restoration subdialogues must guard against another 
potential danger of misrepresentation: that students 
may retain incorrect information even after the 
misrepresentation has been retracted at the close of the 
learning episode. 
4. DETAILS OF THE PMM MODEL 
We have identified several types of pedagogic 
misrepresentations, and have implemented and 
evaluated them in a partial tutorial dialogue system. 
The implemented system concentrates on the function 
of the misrepresentation expert, and therefore the 
dialogue system is not fully functional: for example, 
it does not process or generate surface natural 
language. We have implemented the 
misrepresentation expert and the PMM structures, the 
blackboard communication architecture, the student 
model, and the domain knowledge (see Figure 1). The 
content planner and other system components are 
implemented as shells able to provide necessary 
information when needed. 
Input to the system is a teaching situation 
including information from the content planner, the 
student model, and the domain. The system's output 
is a log of system actions detailing the simulation of 
the teaching situation. 
Figure 3 shows the organization of the 
implemented PMMs, some of which inherit shared 
conditions and effects. The implemented PMMs have 
a variety of uses: Ignore-Specializations PMM 
simplifies concepts by reducing the number of kinds 
that a concept has; Compress-Redirect PMM 
collapses a part of the granularity hierarchy to allow 
specific instantiations of general concepts. There are 
also extended versions of these two PMMs which 
have more wide-reaching effects. The remaining 
PMMs are Entrapment PMM, which uses a 
misconception to corner a student and add weight to 
154 
the illustration of a better conception, and Simplify- 
Explanation PMM, which reduces the complexity of a 
concept's functional explanation. The remaining 
restriction PMM, Restrict-Peripheral PMM, is 
detailed in the following section to illustrate the 
concept of misrepresentation and the elements of the 
PMM model, and to show the PMM's use in an 
actual dialogue. 
Compress- 
,- I \ I E o o'PMM  
\[ Local PMM J~ I ,t"°n,ca.e.P~, s, I Ignore- - - ~ \[ LOCal t'MM \] Specializations 
Extended PMM 
I C°mpress- I Redirect LoCal 
, PMM 
Figure 3. The PMM hierarchy. 
The purpose of the "Restrict Peripheral Concepts" 
PMM is to simplify concepts related to the current 
teaching concept. For example, during an initial 
discussion of base cases (while learning programming 
in Lisp), a student might benefit from a 
misrepresentation which restricts recursive cases to a 
single type, the variety of recursive case used with cdr 
recursion. The restriction allows both participants in 
the dialogue to discuss and refer to a single common 
object, and allows the student to concentrate on base 
cases without needing to know the complexities of 
recursive cases. 
This PMM's preconditions check that there are 
peripheral concepts in the current instructional 
context. Applicability conditions determine whether 
those concepts should be simplified, by considering 
the domain's pedagogic complexity and the student's 
capabilities. For example, the PMM considers the 
difficulty ratings of the current concept and the 
peripheral concept, the student's knowledge of these 
concepts and any existing difficulties with them as 
shown in the student model. In addition, the PMM 
considers other factors such as the student's anxiety 
level and their ability with structural relationships. 
Removal conditions for this PMM consider factors 
such as whether or not instruction about the current 
concept has been completed, or whether the 
instructional context has changed so markedly that the 
PMM can no longer be useful. Revelation conditions 
cover two other cases for a PMM's removal: when 
the student challenges the misrepresentation, and 
when the student or another part of the dialogue 
system requires a hidden part of the domain. 
If applied, the effect of this PMM is to restrict 
peripheral concepts related to the current concept such 
that all but one of their specializations are hidden. 
The PMM carries out the restriction, but does not 
choose the specializations that will remain visible: 
that decision is left to the pedagogic expert, using the 
instructional plan and the student model. 
5. EXAMPLE DIALOGUE 
PMM "Restrict Peripheral Concepts" is illustrated 
below in an example dialogue. The dialogue is based 
on an actual trial of the implemented system, which 
determined when to invoke the PMM, when to revoke 
it, and all the interactions between the knowledge base 
and the dialogue system. However, the surface 
utterances are fabricated to illustrate how the 
misrepresentation system would function in a 
completed tutorial discourse system. 
The teaching domain in the dialogue is recursion 
in Lisp (as shown in Figure 1), and the system 
believes the student to be a novice Lisp programmer. 
T: ... the next thing I'd like to show you is the part 
of recursion that stops the reduction. 
The system's current instructional context contains a 
teaching operator, "prepare to teach x," and a current 
concept, "base case." The current situation satisfies 
the preconditions of PMM "Restrict Peripheral 
Concepts," and its applicability score ranks it as most 
applicable to the situation. The PMM thus 
determines that the peripheral concept "recursive case" 
will be restricted to one specialization, and the 
pedagogic expert chooses 'cdr recursive case' as the 
most appropriate specialization for novice students. 
The system asks the instructional planner to 
replan given the altered view of the domain, and enters 
into an alteration subdialogue with the student. 
Although these subdialogues are only represented as 
stubs in the system's internal notation, the discourse 
could proceed as follows: 
T: Do you remember the last example you saw? 
S: Yes. 
155 
T: OK. Remember that I pointed out the parts of the 
recursive function, the base case and the recursive 
case? 
S: Yup. 
T: Great. Now, I'll just put that example back on for 
a second. You'll notice that the recursive case looks 
like "(t (allnums (cdr liszt)))" Got that? 
S: Yup. 
T: Ok. For when we look at the base case, I want 
you to assume that this recursive case is the only kind 
of recursive case that there is. Then when we write 
some programs, you won't have to worry about the 
recursive case part. Does that sound ok? 
\[At this point the system has already imposed its 
alteration on the knowledge base, and when the 
system asks for the specializations of 'recursive case,' 
it will receive only 'cdr recursive case' as an answer.\] 
S: Sure. 
T: Great. So the thing to remember is, whenever 
you need a recursive case, use a recursive case like 
you have in the example. 
So. Let's move on to looking at the way the base 
case works; let's start with that example we had up. 
First, you identify the base case... 
Later in the dialogue, the student is constructing a 
solution to another problem: 
S: I'm not sure about the base case for this one ... I 
think I'll do the recursive case first. What does the 
recursive case do again? 
T: A recursive case reduces the problem by calling 
the function again with reduced input. The recursive 
case is the default case of the "cond" statement, and it 
calls the function again with the cdr of the list input. 
\[Here the PMM again alters perceived reality, 
restricting 'recursive case' to 'cdr recursive case'\] 
S: Right. 
lisz0))? 
T: Yep. 
So the recursive case is (t (findb (cdr 
\[The PMM is again used to verify the student's 
query.\] 
S: OK. Now the base case ... 
This exchange shows that the misrepresentation is 
useful in focusing the dialogue on the current concept 
of base case, by making the recursive case easy to 
synthesize. 
The system continues investigating and teaching 
base case until the student can analyse and synthesize 
simple base cases. The instructional plan then raises 
its next step, "complete base case." Arrival at this 
plan step satisfies one of the removal conditions for 
the PMM, so the system engages in a restoration 
subdialogue with the student, which might go as 
follows, preparing the student for the next context: 
T: Ok. The next thing we'll do is look a little closer 
at recursive case. Although I told you that there was 
only one kind of recursive case, there are actually 
more. The reason we only used one kind of recursive 
case is because I wanted to make sure you learned the 
way a base case works without needing all the details 
of recursive cases. Recursive cases still do the same 
thing (that is, reducing the input) but the specific 
parts might do different things than the recursive case 
we used. Does that sound ok to you? 
S: ok. 
T: So let's look at recursive cases. We'll only deal 
with the kinds used with cdr recursion .... 
6. RESULTS AND DISCUSSION 
Evaluative trials for the PMM system have been 
aimed specifically at both the individual PMMs and 
the PMM model. Twenty-six different types of 
situations have been designed to test the PMMs' 
relevance, consistence, and coherence. Through these 
trials the individual PMMs demonstrated their 
integrity, and the PMM model itself was shown to be 
capable of working within a dialogue system 
architecture. Full details of evaluation methodology 
and results can be found in \[Gutwin 1991\]. 
This research project has shown that PMMs can 
be represented for use in a tutorial dialogue system, 
and supports their value as a pedagogic tool. 
However, the foremost contribution of the PMM 
system to computational dialogue may be how it 
extends the notion of focus currently used in dialogue 
research. Grosz and Sidner \[1986\] see dialogue as a 
collection of focus spaces which shift in reaction to 
changes in the discourse's purposes and salient 
entities. This research suggests that within any of 
these focus spaces, there can exist a further structure: 
a context that provides a specific interpretation of the 
knowledge represented in the system. The same 
knowledge is "in focus" throughout the focus space, 
but different contexts can color or interpret that 
knowledge in different ways. A pedagogically 
motivated misrepresentation is thus a context 
mechanism that alters the domain knowledge for an 
educational purpose. It is possible that we always use 
156 
some kind of alternate interpretation or 
misrepresentation to mediate between our knowledge 
and other dialogue participants. 
Focusing structure has traditionally been used in 
interpretation: in several projects (\[Grosz 1977\], 
\[Sidner 1983\]), context structures are shown to be 
useful in tasks like pronoun resolution or anaphora 
resolution. Pragmatic contexts, such as those created 
by a PMM, can direct generation of discourse as well. 
They are active reflections of the larger situation, 
rather than local representations of dialogue structure, 
and they are able to alter the discourse in order to 
further some goal. Responding to patterns in the 
world outside the dialogue allows pragmatic context 
mechanisms such as PMMs to consider fitness and 
suitability of a dialogue situation in addition to a 
focus space's subset of goals and salient entities. 
Another issue of importance to this research is 
that of tailoring. While some existing dialogue 
systems tailor an explanation to the user's level of 
expertise (e.g. \[Paris 1989\], \[McKeown et al 1985\]), 
the PMM system instead tailors the domain to the 
learner. The PMM system does not make basic 
decisions about either content or delivery in a 
dialogue, but attempts to shape the content's 
representation into a form which will be best suited to 
the learning situation. 
The PMM model also touches on research into 
multiple representation, in that it provides a 
mechanism for encapsulating several different 
interpretations of a knowledge base. The mechanism 
might be able to model and administer alternate 
representations of other kinds as well, such as 
analogy. 
The usefulness and ubiquity of PMMs also 
suggests that a literal interpretation of Grice's 
maxims, particularly the maxim of quality, is 
inappropriate. Clearly, we often say things we know 
to be false! However, the maxim of quality can be 
rescued by indicating the relationship between truth 
and dialogue purposes: from the original, "do not say 
that which you believe to be false," we create a new 
maxim, "do not say that which you believe to be false 
to your purposes." The new maxim shifts emphasis 
from an absolute standard of truth in dialogue to the 
more pragmatic idea of truth relative to a dialogue's 
goals, and better reflects the way humans actually use 
discourse. 
Much remains to be accomplished in this research. 
There are undoubtedly other as yet undiscovered 
PMMs. The notion of intentional misrepresentation 
itself may just be an instance of a more general 
context mechanism that underlies all dialogue, an idea 
that should be explored by considering other kinds of 
dialogue from the perspective of PMMs, and by a 
closer examination of existing theories of discourse 
context. Finally, all of the oracles used in the PMM 
System should be replaced by functioning components 
so that a dialogue system with complete capabilities 
can stand alone as proof of the PMM concept. 
Nevertheless, this research points the way towards the 
possibility of a new and widely applicable mechanism 
for modelling dialogue. 
ACKNOWLEDGMENTS 
The authors wish to thank the Natural Science and 
Engineering Research Council of Canada for financial 
assistance during this research. 

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