International Journal of Intelligence Science, 2014, 4, 17-23
Published Online January 2014 (http://www.scirp.org/journal/ijis)
http://dx.doi.org/10.4236/ijis.2014.41003
OPEN ACCESS IJIS
An Affective-Motivational Interface for a Pedagogical
Agent
Martha Mora-Torres1, Ana Lilia Laureano-Cruces1,2,3, Fernando Gamboa-Rodríguez4,
Javier Ramírez-Rodríguez2,3, Lourdes Sánchez-Guerrero2
1Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México, México City, México
2Departamento de Sistemas, Universidad Autónoma Metropolitana-Azcapotzalco, M éxico City, México
3Laboratoire Informatique d’Avignon, Université d’Avignon et des Pays de Vaucluse, Avignon, France
4CCADET, Universidad Nacional Autónoma de México, México City, México
[e-mail], [e-mail]
Received October 7, 2013; revised November 8, 2013; accepted November 15, 2013
Copyright © 2014 Martha Mora-Torres et al. This is an open access article distributed under the Creative Commons Attribution Li-
cense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In
accordance of the Creative Commons Attribution License all Copyrights © 2014 are reserved for SCIRP and the owner of the intel-
lectual property Martha Mora-Torres et al. All Copyright © 2014 are guarded by low and by SCIRP as a guardian.
ABSTRACT
This research is framed within the affective computing, which explains the importance of emotions in human
cognition (decision making, perception, interaction and human intelligence). Applying this approach to a peda-
gogical agent is an essential part to enhance the effectiveness of the teaching-learning process of an intelligent
learning system. This work focuses on the design of the inference engine that will give life to the interface, where
the latter is represented by a pedagogical agent. The inferenc e engine is ba sed on an affective-motivational model.
This model is implemented by using artificial intelligence technique called fuzzy cognitive maps.
KEYWORDS
Affective-Motivational Model; Intelligent Learning System; Pedagogical Agent; Affective Computing;
Simulation of E m otions
1. Introduction
Intelligent Learning Systems (ILS) conceive the teach-
ing-learning process (TLP) as a partnership between the
module tutors, whose interactions with the user are re-
presented by a pedagogical agent [1,2]. Therefore, it is
necessary to consider a psychological theory of emotion
that sustains the necessary motivational affective model
to obtain the affective-motivational state of the user. In
the psychology of emotion, there are several theories
whose fundamental differences relate to the definition
and conceptualization of emotion [3]. However, the ele-
ments of the emotion definition show some degree of
convergence among the different theoretical. The theory
of Ortony, Clore, Collins [4], known as OCC theory, spe-
cifies a psychological structure of emotions according to
personal and interpersonal descriptions of events. There-
fore, based on OCC theory, an affective-motivational
cognitive structure is developing [5-10] and used in sys-
tems with artificial intelligence (AI). In this case, rea-
soning system is tied to tutor module, so that the peda-
gogical agent improves the intelligent learning system
(ILS) performance.
In order to achieve it, the integration of affective-moti-
vational cognitive structure efficiently binding to the
TLP is necessary.
This paper is organized in sections. Section 2 explains
the OCC theory underlying the affective-motivational
cognitive structure, Section 3 explains the AI technique
called Fuzzy Cognitive Maps (FCM), which are used to
represent the affective-motivational model (based on the
affective-motivational cognitive structure), Section 4
explains the pedagogical agent interface of ILS, Section
5 explains the affective-motivational cognitive structure
design, Section 6 explains the ILS tests and results, fi-
nally we find Section 7 with conclusions.

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2. Emotions According to the Theory of
Ortony, Clore and Collins (OCC)
OCC theory proposes a general structure which specifies
that there are three main kinds of emotions, the result of
focusing on each of the three highlights of the world:
• Events and their consequences
• Agents and their actions.
• Pure and simple objects.
This establishes the evaluation criteria:
• Goals to evaluate events.
• Rules for evaluating the action of the agents.
• Attitudes for evaluating the objects.
There are three main kinds of emotions specified:
• Emotions based on events: specifying the goals re-
lated to the events.
• Emotions of attribution: attributed responsibility to
the agents about their actions based on rules.
• Emotions of attraction: based on attitudes toward ob-
jects.
The intensity of emotions can be affected by so-called
local and global variables. Thus cognitive representations
of emotions are also modified.
The local variables are variables that affect only one
kind of emotion, for example, in the case of emotions
based on events; the local variable that affects its inten-
sity is the desirability of events and their consequences in
relation to the goals. For attribution emotions, the cor-
responding local variable is the plausibility (approval or
disapproval) of the agent’s actions according to the rules.
Finally Attr action emotions are affected in their intensity
by the attraction of the objects.
Global variables, as the name implies, are variables
that affect the intensity o f all kinds of emotion and there-
fore, cognitive representation. These variables are: 1)
proximity: it attempts to reflect the psychological prox-
imity (in time or space) of event, object or agent that in-
duces emotion, 2) sense of reality refers to the degree to
which the event, agent or object underlying the affective
reaction seems real to the person experiencing the emo-
tion, 3) excitation: the existing level of arousal affects the
intensity of emotions and thus their affectiv e reaction and
4) the unexpected: refers to unexpected positive things
are evaluated more positively than expected and unex-
pected negative things more negatively than expected.
Goals are classified according to OCC theory [4] in
active pursuit goals (AGs), goals of interest (IGs) and
filling goals (FGs). AGs represent the kind of things you
want to get done and include goals set by Schank and
Abelson (1977) named achievement goals (for obtain
certain things), entertainment goals (to enjoy certain
things), instrumental goals (are the instrument for other
goals) and goals of crisis (to handle crises when preser-
vation goals are threatened). IGs represent the kind of
thing you want to happen and thus can generate AGs to
encourage happen. IGs include preservation goals (to
preserve certain states of affairs). FGs are cyclical, even
if achieved, not abandoned. These goals include those
established by Schank and Abelson (1977) like satisfac-
tion goals (meet certain requirements). In case OCC
theory, needs can be biological or otherwise be cyclical.
To emulate the perception of emotions during the ILS-
user interaction, it is considered a pedagogical agent de-
signed with an affectiv e-motivational cognitive structure.
Integrated, this latter, to the ILS inference engine [5-10].
The affective-motivational cognitive structure is built
according to the OCC theory and the implementation of
this structure is realized through FCM [11-15 ].
3. Fuzzy Cognitive Maps (FCM)
The fuzzy cognitive maps (FCM) were introduced by
Bart Kosko [16] to describe the behavior of a system in
terms of concepts and causal relationships between these
concepts. Digraphs FCM are used to represent causal
reasoning in which the nodes are concepts that describe
the main features of the system, and the edges between
nodes establish causal relationships between the concepts.
The diffuse part allows degrees of causality in relation-
ships. FCMs are used as representation technique, due to
its ability to handle inherent uncertainty in the decision
making processes complex, and having a parallel and
distrib uted reas oning [17].
The qualitative approach of the relationship matrix al-
lows us to observe the behavior of the system. However,
you must have a quantification and interpretation with
respect to causation of FCM. This quantification allows
for the next state of each node, by adding effects to all
nodes on the particular node [13,14,18].
Causality Relationships
Causality relationships refer to the effect that a concept
has on the rest of the concepts involved in the description
of an environment. The effect is to increase or decrease
the likeli hood of oc c urre nc e of a not he r concept . The r e fore,
there are two type s of re la tionshi ps: nega tive and posi ti ve.
• Negative: the negative relationship is one in which
the increase in the likelihood of occurrence of an
element causes the proportional decrease in the like-
lihood of occurrence of another element. And the de-
crease in one causes the proportional increase of other.
Is expressed numerically by taking a value within the
range [-1,0).
• Positive: the positive relationship is one in which an
increase in the likelihood of occurrence of an element
causes proportional increase in the likelihood of oc-
currence of another element and a decrease in the li-
kelihood of one causes the proportional decrease in
the likelihood of occurrence of other. For example,
increasing errors originates increased likelihood of
occurrence of frustration. Is express ed numerically by

M. MORA-TORRES ET AL.
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19
taking values in the range (0, 1].
• If there is no effect or it is neutral, the relationship is
expressed as 0 (zero).
4. Interface Design: A Pedagogical Agent
Emotive pedagogical agents are the last generation to
design human-computer interfaces. This kind of agents is
different because their appearance more accurately si-
mulates an animated character or even a human. They
have a guide on how applications and conventions should
be their personality and appearance. Pedagogical agents
are created to support learning by interacting with stu-
dents in interactive learning environments [19,20].
The credibility of these agents build trust relies on the
visual quality of the agent and the behaviors that emulate
humans [21,22].
Animated pedagogical agents facilitate learning in
computer environments. These agents represent animated
characters that respond to actions taken by the user. Fur-
thermore, there is the mode where the latter have the
ability to move within the context of learning, thus pro-
viding useful functions within learning environments
[23].
A pedagogical agent can be invaluable for the user
knowing whether their actions are inappropriate or in-
correct, in which case the agent can intervene. Pedagog-
ical agents show entertaining speeches during the teach-
ing-learning process and can intervene with tips, tactics
didactic introductions and even attention calls [1,24].
The layout design consists of six phases: the Case, is
where you define the user profile and from it creates the
most suitable interface design, including pedagogical
agent prototype, in Problem analyzing user requirements
individually, in this case we use learning styles as a
guideline, in Hypothesis are the profiles created by com-
bining learning styles and color and how these can be
represented graphically, for the Project is selected the
most viable option and create models according to the
specifications in a program for this purpose, in Perform-
ing, animations are developed and inserted into the inter-
face, and finally in the Evaluation end user is used for
use the interface and provide their comments [25].
Affective-Motivational Interface of a
Pedagogical Age nt
Figure 1 shows the affective-motivational interface of
agent designed according to user preferences. These
preferences are identified through a questionnaire which
asked users about the physical characteristics of a pe-
dagogical agent. The actions undertaken by the peda-
gogical agent are based on actions of the inference en-
gine designed on an affective-motivational cognitive
structure representing the TLP [10,12].
Figure 1. Affective pedagogical agent.
5. Affective-Motivational Structure
The affective-motivational structure (Figure 2) shows
the concepts involved in the TLP, such as goals, events,
actions of user or agent, rules and affects. These concepts
are tied to the TLP elements thro ugh facets of motivation
such as: effort, latency, persistence, and choice.
Interest and desire, for example, are especially related
to persistence and effort. Joy, admiration, pride, and the
like, for their part, provide the energy of motivation.
Help is related to choice, and relief affect feeds this
choice.
Strategies are actions based on an instructional goal
emerged as a top goal in the affective-motivational
structure. Therefore, these strategies are called instruc-
tional strategies. Instructio nal strategies can be cognitive
and operative. Cognitive strategies are actions of the
cognitive diagnosis and Operative strategies are appro-
priate actions to drive the instruction, so include strate-
gies to contextualize, to guide, motivate and retain the
user’s attention [24].
To link operative strategies to the affective-motiva-
tional structure is necessary to define the actions to take
in each instructional level related to the type of know-
ledge or skill involved in the proposed task to achieve
instructional objectives [12]. The actions for each in-
structional level are listed according to information and
portrayal that are consistent for each category of genera-
lizable skill (learning category). The strategy is chosen
according to the type of user error, instructional level,
learning category and inferred affect.
Nomenclature of TLP elements:
ID Interest & Desire E Errors
H Help JP Joy-Pride
St Strategies AL Admiration-Like
I Interruption R Relief
Q Quit DR Dislike-Reproach
P Performance FA Frustration-Anguish
L Latency Sh Shame

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Figure 2. Affective-motivational structure.
5.1. Causality Relationships in the
Affective-Motivational Structure
TLP elements are interrelated through causality relation-
ships that indicate the effect that an element has on the
rest of the elements involved in the description of an en-
vironment. The effect is to increase or reduce the like-
lihood of another element appearing. So there are nega-
tive and positive relationships (Table 1).
For example, the ID, according to affective-motiva-
tional model, is positively related to JP, AL and P. This
means that an increase in the likelihood of occurrence of
ID causes the proportional increase in the likelihood of
occurrence of JP, AL and P. Otherwise, a decrease in the
likelihood of occurrence of ID causes the proportional
decrease in the likelihood of occurrence of JP, AL and P.
On the other hand, ID is negatively related to Q, E and
DR. This means that an increase in the likelihood of oc-
currence of ID causes the proportional decrease in the
likelihood of occurrence of Q, E and DR. Otherwise, a
decrease in the likelihood of occurrence of ID causes the
proportional increase in the likelihood of occurrence of Q,
E and DR.
5.2. Causality Matrix
The relationships are represented in a matrix of causali-
Table 1. Causality relationships.
Relationship Related concepts
ID Positive
Negative JP, AL, P Q, E, DR
H Positive
Negative R, St, I, L E
St Positive
Negative AL, ID, P DR, Q, E
I Positive
Negative St, Q ID
Q Positive
Negative FA, Sh, E JP, AL, R, ID, St, P
P
Positive
Negative
JP, AL, R, ID, St
I, Q, L, E, DR, FA, Sh
L
Positive
Negative
St, I
R, P
E
Positive
Negative
FA, Sh, St, I, Q
ID
JP
Positive
Negative
ID, P, AL
L, E, DR
AL Positive
Negative ID, St, Q, L, DR
R
Positive
Negative
St, P
Q, FA
DR
Positive
Negative
St, I, Q, L, E, FA, Sh
ID, P, AL, R
FA
Positive
Negative
Q, E, L, DR, Sh
P, JP, AL, R
Sh
Positive
Negative
St, Q, E, DR, FA
ID, P, JP, AL

M. MORA-TORRES ET AL.
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21
ties (Table 2) based on the description of the positive
and negative relationships.
Causality matrix forms the agent’s inference engine.
The inference engine response is the next state of each of
the elements of the model and is obtained by multiplying
an input vector (state values of the elements that consti-
tute the affective-motivational model) by the matrix of
causality. The resulting vector (output vector) is eva-
luated using the logistic function (Equation (1)) as thre-
shold function [16]. This is repeated until a s table output
vector.
( )
( )
c
11ex
sx −
= +
(1)
where:
S(x) = Logistic function, and represents the bounded
output vec tor.
x = output vector resulting from multiplying the input
vector by the causality matrix and represents the sum of
effects between the elements of the motivational-affec-
tive model.
c = scaling constant = 5.
The logistic function takes the dimension of the result
in the range [0,1]. So that it enables us to interpret each
of the vector values as the likelihood that respective mo-
tivational-affective model element is present [13,14,18].
6. Tests and Results
The prototype of intelligent learning system (ILS) is de-
signed with an inference engine that includes the affec-
tive-motivational model (based on affective-motivational
structure).
Table 2. Causality matrix.
ID H S I Q L Lt E JP AL
R HR DF Sh
ID 0 0 0 0 -1 1 0 -1 1 1 0 -1 0 0
H 0 0 1 1 0 0 1 -1 0 0 1 0 0 0
S 1 0 0 0 -1 1 0 -1 0 1 0 -1 0 0
I -1 0 1 0 1 0 0 0 0 0 0 0 0 0
Q -1 0 -1 0 0 -1 0 1 -1 -1 -1 0 1 1
L 1 0 1 -1 -1 0 -1 -1 1 1 1 -1 -1 -1
Lt 0 0 1 1 0 -1 0 0 0 0 -1 0 0 0
E -1 0 1 1 1 0 0 0 0 0 0 0 1 1
JP 1 0 0 0 0 1 -1 -1 0 1 0 -1 0 0
AL 1 0 1 0 -1 0 -1 0 0 0 0 -1 0 0
R 0 0 1 0 -1 1 0 0 0 0 0 0 -1 0
HR -1 0 1 1 1 -1 1 1 0 -1 -1 0 1 1
DF 0 0 0 0 1 -1 1 1 -1 -1 -1 1 0 1
Sh -1 0 1 0 1 -1 0 1 -1 -1 0 1 1 0
The ILS is tested a first group of college students
enrolled in structured programming (application domain).
The tests consist of solving scenarios representative of
application domain teaching-learning process.
Scenarios are tasks classified according to learning
category and instructional level corresponding with the
instructional objectives of structured programming.
Performance results are compared with those obtained
by a second group of college students who used the ILS
designed with an inference engine excluding the affec-
tive-motivational model. The results are summarized in
the graph of F ig ures 3(a) and (b).
The results obtained with the ILS which included an
affective-motivational model, improved by 6% compared
to those obtained with the ILS did not include the model.
7. Conclusions
The contribution of this work to affective computing lies
in the model used to choose strategies (FCM). This mod-
el can provide approximate answers to what happens in
(a)
(b)
Figure 3. (a) ILS without affective-motivational model; (b)
ILS with affective-motivational model.

M. MORA-TORRES ET AL.
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22
the environment with the cognitive and affective state of
the user due to the parallel distribution of causality. This
improves the user interaction with the system.
FCM modeling the behavior exhibited in the strategies
related to the cognitive affective-motivational structure.
This structure feeds the student and the tutor modules,
to which it provides clues to the user’s emotional state.
This helps in choosing the affective-cognitive strategy
that the pedagogical agent will deploy, thereby maximiz-
ing the effectiveness of the intervention.
Acknowledgements
This paper is part of the research being carried out by
Martha Mora-Torres to obtain her PhD in the Posgrado
en Ciencia e Ingen iería de la Computación at th e Univer-
sidad Nacional Autónoma de México. It is supported by
CONACYT (CVU: 167259). Also, this project is part of
the Soft Computing and Applications research (Emo-
tions), funded by Universidad Autónoma Metropolitana.
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