Integrating a Believable Layer into
Traditional ITS
Sassine Abou-Jaoude
Claude Frasson
Computer Science Department
Université de Montréal
C.P. 6128, Succ. Centre-Ville
Montréal, Quebec Canada
{jaoude, frasson}@iro.umontreal.ca
Abstract. Adding believability and humanism to software systems is one of
the subjects that many researchers in Artificial Intelligence are working on. We,
and from our interest in Intelligent Tutoring Systems (ITS), propose adding a
believable layer to traditional ITS. This layer would act as a user interface,
mediating between the human user and the system. In order to achieve
acceptable believability levels, we based our work on an emotional agent
platform that was introduced in previous works. Our ultimate aim is to study the
effect, from a user's perspective, of adding humanism in software systems that
deals with tutoring.
1. Introduction
We work mainly on research and development that revolve around Intelligent
Tutoring Systems and how to make these systems more efficient. Works that aims at
increasing the efficiency of ITS are mainly conducted under the following guidelines:
Student Model. Building a more representative student model [1] that would project a
thorough image of the user in front, thus permitting the system's adaptability to be more
reflective of the actual user's status.
Curriculum. Constructing a more efficient curriculum [2] module that would work as a
skeleton for teaching subjects.
Tutorial strategies. Working on different cooperative tutorial strategies [3], through the
amelioration of existing ones, the introduction of new ones, such as the troublemaker [4],
and the possibility of switching strategies in real time interactions [5].
It was a natural progressive step, for us, to start exploring the integration of
humanism and believability in ITS, since we highly believe, that it will increase the
system's performance. For this, we suggest the addition of a believable layer in traditional
systems in order to create, what we call a believable ITS (see fig. 1).
Traditional
ITS
Intelligent
Tutoring
System
Believable
ITS
Traditional
communication
route
Believability Layer
USER
Figure 1: Introduction of the believable layer
This layer will play the mediating role between the user, an entity that is very much
influenced by humanism in its normal behavior, and the system that is normally not
capable to interpret humanism on its own.
Ultimately we would say that our believable agent platform is complete if a user being
tested by a system, of which he has no direct sight, would not be able to tell if the latter is
a human or a machine (i.e. passing the Turing test somehow).
Adding believability means the introduction of human aspects in the software
agent such as personality, emotions, and randomness in behavior.
The nature of randomness itself makes it somehow, an achievable goal. One way
to achieve randomness is through the use of well-known software procedures that would
produce random output mapped on the sample behavioral space of the agent. Moreover,
since no rules or standards may govern randomness any proposal suggested at different
levels may be considered as an additional randomness entity to the system. For the
character and emotions; the integration is not as evident. They were subject to much
research. And the line of division between these two entities is not as clear too.
Therefore, most researchers approached the emotional aspect and the personality traits as
one entity. Among these research group we would like to mention the contribution of the
following people: C. Elliot and co. for their work on the affective reasoner [6], a platform
for emotions in multimedia. J. Bates and co. at Carnegie Melon University, for their work
on agent with personalities and emotions [7] and the role of the latter's in assuring
believability.
Barbara Hayes-Roth and her team at Stanford, for their work on the virtual theater [8] and
agent improvisation. Pattie Maes and her team at MIT [9] for their work on virtual world
and the emotional experience in simulated world.
As for us, we believe, for reasons that will be presented in the next section
(section 2), that emotions might be the major entity in creating believability. And
integrating a believable layer in ITS, would be almost reached by adding emotional
platforms to main agents in this ITS. Section 3 will allow us to introduce our emotional
agent platform, the emotional status E and the computational model that would allow
the agent to interact emotionally in real time following different events that might take
place in his micro-world (in our case the ITS). Section 4, is a case study in which the
theory proposed in section 3 will be applied to a student model in a competitive learning
environment (CLE). Finally, in section 5, we will be concluding by presenting our future
work and the main questions that we are planing to treat in our future experiments.
2
2. Believable layer vs. Emotions
The use of the term "emotions" in our work is done to its widest meaning. An
emotion is not simply a feeling that an agent X might have to an agent Y (i.e. hate, like,
etc.). It is more, a submersible status in which an entity (human or agent in our case) is,
that would influence its behavior. Instead of limiting our choice to the classical definition
of emotions we widened it, to include many types, based mainly on previous work done
by Ortony and Elliot [10, 11], such as well-being (i.e., joy, distress, etc.), prospect-based
(i.e. hope, fear, etc.), attraction (i.e. liking, disliking, etc.) and etc.
We also introduced in previous work [12] the notion of stable emotions as a mean of
defining personality traits, and henceforth personality.
Although interpretation of emotions differs among cultures, backgrounds and even
individuals themselves, the sure thing, is that humans are the most reliable source we
know for this interpretation. This human ability has always given human teachers
advantages on their machine's counterpart [12].
In ITS emotions enhance believability, because they permit the system to simulate
different aspects that the user had previously only encountered in a human teacher.
Among these aspects we mention the following:
1) The engagement of the user, which is achieved when the system wears different
personalities, that would interest the user, to discover. 2) The fostering of enthusiasm in
the domain that is also achieved by the capacity of the system to show its own interest in
the subject through the variations of its emotions. And 3) the capacity to show positive
emotions as a response to the users positive performances.
We believe that the wide definition and the flexible classification we gave to emotions
allow us to approach believability as analog to emotional. And to us, creating a believable
layer is in fact building an emotional agent platform. Yet, we are totally aware, that the
ultimate measurement of the righteousness of our choice will be provided by the final
results of the system.
Particularly in ITS, adding a believable layer (such as fig. 1 shows) would in fact be
narrowed down to adding additional emotional layers to agents that might exist in the
simulated micro-world of the ITS (i.e. A tutor, a troublemaker, a companion, and a
student model, etc.). The existence of these actors depends mainly on the tutorial strategy
of the system.
Traditional layer that would
involve the knowledge level,
introduced in 1996 [1]
Student Model
Layer 1
Layer 2
Layer 3
Knowledge level
&
Cognitive model
Layer that involves the
learning preferences
introduced in 1998 [5]
Learning profile
Emotions
&
Believability
The believable layer that will
include emotions,
personality, and randomness
in behavior. Introduced
hereby.
Figure 2: Student model with layers added chronologically
3
The student model is one of the entities that exists in many strategies, therefore
for illustration purposes we show in figure 2 a diagram of our student model and its
chronological evolution. As figure 2 shows, the final layer is the emotional layer. This
layer will be the mean to produce believability in the system as a whole.
3. The emotional agent platform
Lately our research has been concentrated on creating a computational system for
emotions. A system that would be used to create, calculate and constantly update the
emotional status of the agent while interacting with its environment. This section (section
3) will present the general computational model for an emotional platform and the next
section (section 4) will present a case study in which the theory is implemented in a
particular scenario where an emotional agent is reacting in a competitive learning
environment.
3.1 The emotional couple
The basic entity in our system is the emotional couple ei. An emotional couple is
a duo of two emotions that belong to the same group [11] but contradict each other. If Ei1
and Ei2 are two emotions that satisfies the condition just stated, than we can write:
ei  [Ei1/Ei2] is an emotional couple made of these two emotions
As an example, the two emotions Joy and Distress belongs to the same group
(appraisal of a situation) and they have contradictory exclusive interpretations (while Joy
is being pleased about an event, Distress is being displeased). Joy and Distress will make
an emotional couple e1 = [Joy/Distress].
The value of an emotional couple is a real number that varies between -1 and +1
inclusively. When this value is equal to +1, it would be interpreted that the left emotion in
the couple is being experienced to a maximum. When the value is equal to -1, it would be
interpreted as the emotion on the right side of the couple is witnessed to a maximum. A
zero would mean that concerning this group of emotions the agent is indifferent. Most of
the times the value of the couple is floating between those limit values.
Formally we would have:
ei  [Ei1/Ei2]  [-1,1]
Where: i  
ei = +1  Ei1 = +1 et Ei2 = -1 || ei = -1  Ei1 = -1 et Ei2 = +1 || ei = 0  Ei1= Ei2 = 0
3.2 The emotional status
The emotional status E is the set of all the emotional couples that an agent would
have. The emotion status is somehow function of the time t (however this variation with
t is not continuous, it is somehow discrete, since it awaits an event) :
E  [e1, e2, …, ei, … , en] where
4
i, n  
The new emotional status E' is computed every time an event takes place. It is
function of the previous status E, and the set of the particularities P(s) that the context
posses (see section 4). These particularities are external factors that influence the
emotional status yet belong to external entities other than the emotional layer.
As an example the performance of the user in an ITS would be an external factor that
would influence its emotional status but is not a part of it. Again we define P(s) as the
set of these factors pi, therefore we have:
P(s)  [p1, p2, …, pi, … , pm] where
i, n  
Finally we can write:
E' = f { E, P(s) }
3.3 The computational matrix
In order to compute every element ei' of E', we will be creating a computational
matrix M. M will have elements aiJ that will determine the weight of how much each
constituents affects the emotional couple ei'. The choice of the values of aiJ is a very
critical issue since it is basically the core of the whole emotional paradigm. The next
paragraph will present the experimental way by which these weights, in the matrix M are
to be determined. For the moment, we know that M has the following dimensions:
|M| = |E| x |E + P(s)| = n x (n+m)
And it has the following shape:
E
E'
e 1'
...
e n'
e1
a11
...
...
...
...
aaijij
...
P(s)
en
...
...
...
p1
...
ai n  1
...
... pm
... a1m
... ...
... anm
Now we can calculate E' as the set of ei, where ei:
n
m
j1
j n 1
ei aijej aijpj
5
Provide thorough
explanation of ei, pi,
and values allowed
Tolerate misunderstanding in
order to produce a level of
humanism and indecision
Test the user
Fail
Pass
Provide the
initial emotional
st. E
Events
User provides E'
Repeat With E'
Register E'
Quit
Figure 3: Block diagram of the backtrack test allows the
determination of the weights aiJ in M.
3.4 The weight factor and the procedure
Upon determining P(s) we will proceed to determine the weight aiJ
experimentally. The experiment is a backtrack procedure (see fig. 3 for details). Normally
E and M are known and we proceed to calculate E'. But at this level M is the target.
Human users will help determine M. In details, users are given explanations about the
system's entities (ei, pi, and permissible values), and then they are tested to see if they
assimilated their meanings. In this test, as figure 3 suggests, we tolerate a certain level of
misunderstandings in order to simulate the randomness and the indecision in human.
Starting with E, the user is asked to provide the new E' following a certain event,
according to the user's best judgement. Note that the idea of human making choices is
exactly what we wanted in our system, since we are aiming at creating systems that
imitate humans. Repeating this procedure (see fig. 3) will allow us to create enough
equations to solve for aiJ in a system of n*m equations with n*m variables.
4. Case Study: An emotional student model in a competitive learning environment
(CLE)
6
To test all thus, we proceeded in adding an emotional layer to the traditional
student model (see figure 2), in a simulated learning environment. There are three main
actors in the system: the emotional agent in question, the troublemaker (a special actor
who sometimes mislead the student for pedagogical purposes [5]) in the role of a
classmate and the tutor. In the story: the tutor will ask a question of value V to both
students. The troublemaker will provide an answer to the tutor Rpt, of which the
emotional agent has no knowledge. The troublemaker will then propose an answer to the
emotional agent Rpe. At this point, the emotional agent will have to provide his answer
Ret to he tutor. Once this exchange is finished the emotional agent have access to Rpt.
And following this, his emotional status will be recalculated. Figure 3 shows the
environment of the emotional agent, with different variables that will enter in the
calculation of E.
Question with
value
V  {1,2,3}
TUTOR
The Troublemaker's answer
The Emotional Agent 's
answer Ret
EMOTIONAL AGENT
1- Emotional Status E
2- Deception Degree dd
Rpt
Troublemaker's
answer to
emotional
agent
3- Perceived Performance (Pp)e
TROUBLEMAKER
1- Performance Pp
Rpe
4- Performance Pe
Figure 3: Simulation of an emotional layer added to the user model in a CLE
The particularities (i.e. pi) of the system that affects the calculation of the
emotional status should be determined experimentally based on human judgements, the
way M was. In our system we identified three main factors that might affect E. The
performance of the emotional agent Pe, the performance of the troublemaker as perceived
by the user (Pp)e, and the level of disappointment also known as the deception degree
dd.
4.1 Value of question vs. Value of answer
To simulate the fact that different goals of the agent might have different priorities
we propose to add a weight V to each question asked by the tutor. The answers have a
value of -1 for wrong answers and +1 for correct one, therefore:
V  {1, 2, 3}
&
Rij  {-1, +1} where +1 = correct answer & -1 = wrong answer
4.2 The performance of the agent and the perceived performance
7
Pe is the performance of the believable agent himself, this performance is also
computed in real time taking into consideration its previous value, the value of the
question and the value of the answer.
P'e =
(1 - V / 8 ) x Pe + (V / 8) x Ret
The factor 8 is also an experimental value determined by a human user sample and
rounded in the formula of Pe'.
(Pp)e is the perceived performance of the troublemaker as seen by the believable
agent. It is normal that the emotions of the agent play a significant role in the calculation
of this entity. We propose the following:
(Pp)'e = f { (Pp)a , Pp , E(t) }
Also experimentally we managed to approximate this function to the following:
1
1
(Pp)'e = [ (Pp)e + Pp + e13 ] (see table 2, for e13)
4
4
4.4 The deception degree
We define the deception degree dd as the degree of disappointment that the agent
is witnessing in his interaction with the troublemaker. The value of dd also varies
between -1 and +1. Upon explaining the factor and its extreme values (i.e. -1 and +1) we
proceeded by providing the users with different combinations of Rpt, Rpe, Ret and C and
asked them to provide values for dd based on their own judgement.
Rpt
Rpe
Ret
C*
dd
-1
0
1
0
1
0
1
0
1
-0.4
-0.5
-0.2
**
0.4
-1
+1
-1
-1
+1
+1

Rpt
Rpe
Ret
C
dd
-1
0
1
0
1
0
1
0
1
-0.7
-1
-0.4
-1
+1
+1
-1
+1
0.8
1
+1
0.3
0.5
1
C is a factor that tells if the agent chose the troublemaker answer (c = 1)
or not (c=0)
** Table
Gray 1:
square
means impossibility
of the situation
Experimental
values of the deception
factor
4.5 The emotional status
In our model we have defined 13 (see table 2) emotional couples
shown in table below. These emotional couples are based on the work of Ortony and
Elliot [10,11]. Therefore the emotional status E is the set of values of those 13 emotions.
8
Joy/Distress (e1)
Satisfaction/Disappointment (e5)
Liking/Disliking (e9)
Jealousy/-Jealousy (e13)
Happy-for/Resentment (e2)
Relief/Fears-Confirmed (e6)
Gratitude/Anger (e10)
Sorry-for/Gloating (e3)
Pride/Shame (e7)
Gratification/Remorse (e11)
Hope/Fear (e4)
Admiration/Reproach (e8)
Love/Hate (e12)
Table 2: Emotional couples used in our case study
4.6 Solving for M
Knowing E and P(s) we proceeded experimentally to solve for M. In this case
we had 208 variables. This will require a minimum of 208 equations of the form
13
ei'   aij  ej  ai14 Pa  ai15 ( Pp)a  ai16 dd
j1
4.7 Results
The results we obtained for the matrix M and its final shape were under
development by the time we first produced this article, now these results are ready and
will be presented in the conference’s presentation and in future works.
5. Conclusion (The believable tutor)
Another major experience that we are working on, is the creation of a mini ITS
based on the classical tutor-tutee strategy. Two models of the tutor will be explored, a
model in which the tutor is an agent with no emotional nor believability aspects, and
another one where a believable layer is added to the tutor. Our aim is to compare the
performances of the ITS under both models. We have already started working on this
application, and preliminary results lead us to believe that believable tutors influence
users performances depending on the latter's knowledge levels.
System performance
123-
Believable ITS
4-
Does this crossing occur?
If yes. Where?
Effect of learning profiles?
Effect of the curriculum?
Regular ITS
Knowledge level
Starter
Novice
Intermediate
Expert
Figure 4. Traditional vs. Believable ITS performances and the pre-estimated
crossing between the two.
9
In details (see figure 4), a believable tutor would be very successful with users who have
a low to average knowledge level, but less appealing to experts.
Still many questions will be answered when the system is implemented. We will
be interested in answering questions such as:
1- Early applications tends to tell that the believable layer in a ITS would reduce the
performance of an expert, who is more interested in direct application, than emotional
systems. Does a crossing between the two ITS (believable and regular) exist?
2- If it does. Then where does it occur? In other terms, at which level of knowledge it is
better to switch to the regular ITS?
3- The knowledge level affects the crossing point of the two systems. Does the learning
preferences or learning profiles of the user affect it too? If yes, then how?
4- Does the choice of the curriculum affect the crossing point?
Acknowledgments
We thank the TLNCE (TeleLearning Network Centers of Excellence in Canada)
who have supported this project.
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[8]
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