A COGNITIVE MODEL FOR HUMAN BEHAVIOR SIMULATION IN

EBDI VIRTUAL HUMANS

H´ector Orozco, F´elix Ramos, Victor Fern´andez, Octavio Guti´errez

CINVESTAV del I.P.N., Unidad Guadalajara, Av. Cient´ıﬁca 1145, Col. El Baj´ıo 45015 Zapopan, Jal., M´exico

Marco Ramos

Universidad Aut´onoma del Estado de M´exico, Cerro de Coatepec, s/n Ciudad Universitaria 50130 Toluca, M´exico

Daniel Thalmann

Ecole Polytechnique F´ed´erale de Lausanne, EPFL IC ISIM VRLAB Station 14 CH-1015 Lausanne, Switzerland

Keywords:

Human behavior simulation, Virtual humans, Personality, MMPI, EBDI agents.

Abstract:

In this paper, we present a new cognitive model based on Psychology for simulating human behavior in

realistic virtual humans. To do this, we use the Minnesota Multiphasic Personality Inventory (MMPI), taking

into account the personality scales deﬁned in it to endow the virtual humans with a real personality and form a

set of fuzzy rules used to obtain the emotional inﬂuences that modify virtual humans’ affective state according

to their personality and the events they perceive from their environment. We also implemented an EBDI-based

action selection by using an event calculus deﬁnition. This action selection mechanism allows virtual humans

to perform actions based on their current emotional state, their beliefs, their desires and their intentions. These

actions deﬁne virtual humans’ behavior for each situation they experience in the environment. As case study,

we present an scenario where a male virtual human with a psychopathic personality and a female virtual human

with a hysteric personality are interacting in a real way.

1 INTRODUCTION

As human beings, our daily life is inﬂuenced at each

time by stimuli received from the environment. These

stimuli generate emotional inﬂuences that change our

internal affective states and behavior, which give us

the ability to generate an almost immediate emotional

response to the different situations experienced in the

real world. In Psychology, one of the most studied

human characteristics that inﬂuences individuals’ be-

havior is Personality. Personality is often deﬁned as

a set of psychological traits and mechanisms within

the individual, which are organized and relatively sta-

ble over time. These psychological traits and mech-

anisms inﬂuence individual’s interactions and adap-

tations to the environment intraphysical, social and

physical. Psychological or personality traits are de-

ﬁned as forms of persistent patterns for perceiving,

relating and thinking about the environment and one-

self. These features distinguish a person from another,

and they are reﬂected in individuals’ behavior in a

wide range of contexts such as social and personal.

In this paper, we present a behavior model based

on Personality that endows virtual humans with the

ability to interact and behave in their environment ac-

cording to their emotional and mood states. In our

approach, we use the Minnesota Multiphasic Person-

ality Inventory (MMPI) (Tellegen et al., 2003) (Sell-

bom et al., 2008). This inventory is one of the most

widely used personality test in the ﬁeld of Psychol-

ogy. The main objective of applying this test is to

identifying with a high degree of reliability and accu-

racy both the individual’s personality proﬁle (person-

ality traits) and the detection of his/her possible psy-

chopathologies (emotional disorders). The 10 person-

ality scales evaluated by the MMPI are: hypochondri-

asis (Hs), depression (D), hysteria (Hy), psychopathic

deviate (Pd), masculinity-femininity (MF), paranoia

(Pa), psychasthenia (Pt), schizophrenia (Sc), hypo-

mania (Ma), and ﬁnally, social introversion (Si). We

take into account these scales to form a set of fuzzy

rules. This fuzzy set is used to obtain the emotional

104

Orozco H., Ramos F., Fernández V., Gutiérrez O., Ramos M. and Thalmann D. (2010).

A COGNITIVE MODEL FOR HUMAN BEHAVIOR SIMULATION IN EBDI VIRTUAL HUMANS.

In Proceedings of the 2nd International Conference on Agents and Artiﬁcial Intelligence - Agents, pages 104-111

DOI: 10.5220/0002738001040111

 SciTePress

inﬂuences that modify virtual humans’ affective state

according to their personality and the events they per-

ceive from their environment. We also implement an

EBDI-based action selection to endow the virtual hu-

mans with the ability to behave and perform actions

based on their current emotional state, beliefs, desires

and intentions. These actions deﬁne virtual humans’

behavior for each situation they experience in the en-

vironment.

The present work is organized in the following

way: Next section presents an overview of the most

important models of personality and emotion applied

to intelligent agents’ behavior. In third section we

propose a new cognitive model based on Personality

to simulate human behavior in virtual humans. Fi-

nally, in last section we will give our obtained con-

clusions from this work.

2 MODELS OF PERSONALITY

AND EMOTION APPLIED TO

BEHAVIOR OF AGENTS

Traditionally, the OCC model (Ortony et al., 1988)

has been considered as the standard model for emo-

tion synthesis and the best categorization of emotions

available. In this model emotions are interpreted as

reactions (positive or negative) to either consequence

of events, or actions of agents, or aspects of objects.

The OCC model explains human emotions and tries to

predict under certain situations, which emotions can

be investigated. Though this model is rather good, it

does not explain completely the origin of the emo-

tional processes and does not present how to ﬁlter

mixed emotions to obtain a coherent emotional state.

On the other hand, most of the proposed person-

ality models are based on trait theories, because the

conversion from trait dimensions to an efﬁcient com-

putational model is very easy. These models con-

sist of a set of dimensions, where each dimension

represents a set of personality traits. The OCEAN

model (Costa and McCrae, 1992) is one of the most

widespread. This model groups personality traits in

ﬁve dimensions: Openness, Conscientiousness, Ex-

traversion, Agreeableness and Neuroticism. Each di-

mension represents a set of speciﬁc personality traits

that correlate together. Although the OCEAN model

is widely accepted, it has many criticisms, because it

does not exactly indicate how Personality affects the

human behavior based on the obtained stimuli and ex-

perienced situations.

Searching for a better model of emotion, FLAME

(Fuzzy Logic Adaptive Model of Emotions) (El-Nasr

et al., 2000) is used to produce emotions and simu-

late the emotional intelligence process. This model

is based on fuzzy rules used to explore the capabil-

ity of fuzzy logic for modeling the emotional pro-

cess. These fuzzy rules are used for mapping from

events to emotions and from emotions to behaviors.

In (Liu and Lu, 2008) is presented a computer model

of motivation. This model integrates personality, mo-

tivation, emotion, behavior and stimuli together. In

spite of the fact that this model shows how motiva-

tion and Personality drive a virtual character’s emo-

tion, it only gives a primary outline for a motivation

model and it is restricted to be tested by a 3D facial

animation system. A new framework based on Artiﬁ-

cial Intelligence for decision making is introduced in

(Iglesias and Luengo, 2007). This framework is used

to produce animations of virtual avatars evolving au-

tonomously within a 3D environment. The exposed

animations in this framework are not very realistic,

because the avatars follow a behavior pattern from the

point of view of a human observer.

A model of individual spontaneous reactions for

virtual humans is proposed in (Garc´ıa-Rojas et al.,

2008). This model was deﬁned by analyzing real

people reacting to unexpected events. This model

presents a semantic-based methodology to compose

reactive animation sequences using inverse kinemat-

ics and key frame interpolation animation techniques.

Nevertheless, this model was created in a subjective

way in according to authors’ personal judgment. In

addition, the reaction types and animation sequences

virtual humans perform are not validated from the

point of view of psychology, but the obtained results

are satisfactory. A different mechanism to add a dy-

namic personality and personality trait openness into

agents is presented in (Ghasem-Aghaee and Oren,

2007). This work is based on the fact that personal-

ity trait openness has implications on cognitive com-

plexity and the decision making ability of agents in

problem solving. In this paper also it is implemented

a fuzzy agent to show personality descriptors, per-

sonality factors, personality style and problem solv-

ing success consequently. In addition, it is showed

a prototype system to demonstrate how personality

trait openness affects agents’ problem solving ability.

Other computational model of personality with per-

sonality change is explained in (Poznanski and Tha-

gard, 2005). This model uses a neural network for

simulating personality over time and intends to be

used as an application in a Sim-type video game. But,

for this work it is necessary to establish a set of psy-

chologically inspired rules to determine which situa-

tions change personality and in what ways.

Due to the direct correspondence between emo-

A COGNITIVE MODEL FOR HUMAN BEHAVIOR SIMULATION IN EBDI VIRTUAL HUMANS

105

tions and facial expressions (Hong, 2008), many re-

searchers prefer to employ Ekman’s six basic emo-

tions (Ekman, 1994) (anger, disgust, fear, happiness,

sadness and surprise) for facial expression classiﬁca-

tion and the OCEAN model, or else the OCC model

in combination with the OCEAN model. The mutual

dependence between emotions and personality is of-

ten represented by Bayesian belief networks (Ball and

Breese, 2000) (Kshirsagar and Magnenat-Thalmann,

2002). Another interesting approach that describes

emotions, mood, personality and their interdependen-

cies using vector algebra is proposed in (Egges et al.,

2004). Many of the revised works, which address

the use of personality in the behavior of virtual crea-

tures, make mistakes when assigning random values

to the different basic personality traits. The alloca-

tion of these values cannot be supported, because the

used theoretical framework does not make sense from

a psychological point of view, indicating that so far

none of the existing work provides an accurate and

reliable mechanism for modeling human behavior. In

this paper, our approach is more different because we

are inspired by psychological studies abouthuman be-

ing’s personality and we use the obtained results from

these studies to endow virtual humans with a realistic

personality, which allows them to behave in a more

intelligent way in their environment in according to

their affective state, beliefs, desires and intentions.

3 COGNITIVE MODEL FOR

HUMAN BEHAVIOR

SIMULATION

In order to generate a better behavior model for vir-

tual humans, in this work we apply the main ideas ex-

pressed by Paul D. MacLean in his model triune brain

(MacLean, 1973). Thus, we propose a new model of

three interrelated layers to generate behaviors inﬂu-

enced by Personality. A brief view of the operating

cycle of our model is the following (see ﬁgure 1): the

virtual human obtains information and stimuli from

its environment through sensors. Thus, based on its

personality and its perception, it interprets them to de-

cide whether it has perceived an event that catches its

attention. Thus, once an event has been perceived,

this is processed unconsciously generating an emo-

tional response and an instinctive reaction that can be

of two types: the ﬁrst one is a reﬂex reaction (for ex-

ample when we touch a hot object with the hand our

reaction is immediately withdraw it without having

previously thought) and the second one is an instinc-

tive reaction of protection (for example, when some-

body suddenly throws us an object, our reaction is to

avoid the blow that can cause us the object). Immedi-

ately in parallel to the generated emotional response

and instinctive reaction, the virtual human becomes

aware of the perceived event and it searches for an

explanation by looking for information from its be-

liefs and its long and short term memory. Once the

virtual human collects the necessary information, it

shows a behavior consistent with its personality. In

this way and based on its desires and intentions, the

virtual human executes actions that it deems the most

appropriate to the situation. Finally, the virtual human

is capable of evaluating the obtained results from its

exhibited behavior and learns about them.

Figure 1: Cognitive model for human behavior simulation

in EBDI virtual humans.

3.1 Fuzzy Rules for Generating an

Emotional Inﬂuence

We use a fuzzy rule-based model composed by a set

of if-then rules. This set of rules is used to gener-

ate the emotional inﬂuence that changes virtual hu-

mans’ emotional and mood states according to their

personality and the events they perceive from the en-

vironment. We use fuzzy logic, because the perceived

events, the emotional inﬂuence (a six-dimensional

vector with corresponding values for each of the six

basic emotions) and the personality have fuzzy lim-

its. This permits changing virtual humans’ emotional

and mood states in a more natural manner, gener-

ating more realistic behaviors. We use a set of in-

put variables corresponding to each personality scale,

for these scales we deﬁne ﬁve fuzzy sets: very low,

low, medium, high, and very high. An event is repre-

sented with an input variable, which has seven deﬁned

fuzzy sets: negative high, negative medium, nega-

tive low, neutral, positive low, positive medium, and,

positive high. The impact of an event over each emo-

tion in the emotional inﬂuence vector is described

by the following fuzzy sets negative very high,

negative high, negative medium, negative low, neg-

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106

ative very low, neutral, positive very low, posi-

tive low, positive medium, positive high, and, posi-

tive very high.

In order to obtain the emotional inﬂuence vector,

we used jFuzzyLogic (Cingolani, 2009), which is a

java package that offers a complete fuzzy inference

system (FIS). This package implements a fuzzy con-

trol language speciﬁcation according to IEC 1131-7

(IEC 1131, 1997). We deﬁned in this language the

input and output variables and the set of fuzzy rules

used to obtain the emotional inﬂuence. For exam-

ple, some representative fuzzy rules written in FCL

for the Hy (hysteria) personality scale are: IF Hy

IS high AND event IS positive high THEN anger IS

negative high, disgust IS negative medium, fear IS

negative low, happiness IS positive low, sadness IS

negative low, surprise IS positive low and IF Hy IS

very high AND event IS negative low THEN anger IS

positive medium, disgust IS positive low, fear IS pos-

itive very low, happiness IS negative very low, sad-

ness IS positive low, surprise IS negative very low.

As example, ﬁgure 2 shows the deﬁned fuzzy sets

for the variables Hy (hysteria), event, and anger, re-

spectively.

Figure 2: Deﬁned fuzzy sets for the variables Hy (hysteria),

event and anger.

3.2 Affective State Update

We propose a process to update virtual humans’ emo-

tional and mood state by using the 10 personality

scales deﬁned by the MMPI, the six Ekman’s basic

emotions (anger, disgust, fear, happiness, sadness and

surprise) and three basic moods (good, neutral and

bad). For this matter, we consider the virtual humans

as entities with a constant personality and a dynamic

behavior, which is constantly changing over time t.

Therefore, the virtual humans’ personality p is ini-

tialized with a set of constant values at time t = 0,

and their emotional and mood states, e

and m

respec-

tively, are dynamic over time and these are initialized

to 0 at time t = 0. We formalize these concepts as

follows:

= [Hs, D,Hy, Pd, MF, Pa, Pt, Sc, Ma, Si], (1)

Hs, D, Hy, Pd, MF, Pa, Pt, Sc, Ma, Si ∈ [0, 1]

Where Hs (hypochondriasis), D (depression), Hy

(hysteria), Pd (psychopathic deviate), MF (masculin-

ity/femininity), Pa (paranoia), Pt (psychasthenia), Sc

(schizophrenia), Ma (hypomania), and Si (social in-

troversion), are the 10 personality scales deﬁned by

the MMPI.

Emotional state e

represents the intensities of the

six Ekman’s basic emotions at each time t. These

emotions are labeled as: an (anger), di (disgust), fe

(fear) ha (happiness), sa (sadness), and su (surprise)

in a 6-dimensional vector in the following way:







[an, di, fe, ha, sa, su] if t > 0

0 if t = 0

an, di, fe, ha, sa, su ∈ [−1, 1]

(2)

In a similar way, the mood state m

represents the

intensities of three basic moods at each time t. These

moods are labeled as: gd (good), nl (neutral) and bd

(bad) in a 3-dimensional vector:



[gd, nl, bd],gd, nl, bd ∈ [−1, 1] if t > 0

0 if t = 0

(3)

We also use an emotional history ω

and a mood

history σ

, which contain the emotional states e

un-

til e

and the mood states m

until m

, respectively.

The next step is to update the emotional and mood

states. To do this, we use an emotional inﬂuence vec-

tor a, which is obtained by applying the fuzzy rules

deﬁned above. This vector contains a desired change

of intensity for each of the six basic emotions. Thus,

when a virtual human has assessed the emotional in-

ﬂuence, the emotional and mood states are updated

A COGNITIVE MODEL FOR HUMAN BEHAVIOR SIMULATION IN EBDI VIRTUAL HUMANS

107

in two steps. The ﬁrst step consists in updating the

emotional state. The second step consists of updating

the mood state. The emotional state is updated taking

into account the last mood as follows:

t+1

= e

+ Ψ

(p, σ

, a) + Ω

(p, ω

, σ

) (4)

We deﬁne a 6× 10 Personality-Emotion Inﬂuence

Matrix P

(how personality inﬂuence emotions). This

matrix is deﬁned once and it is multiplied with the

vector p to obtain a new vector u. We use this vec-

tor to construct a diagonal matrix P (how strong an

emotion can be given a personality). Thus, we com-

pute a 6× 3 Mood-Emotion Inﬂuence Matrix T (how

moods inﬂuence emotions) that is multiplied with the

current mood m

to obtain the mood inﬂuence on the

ﬁnal emotional state. Thus, we obtain the following

deﬁnition for the function Ψ

(p, σ

, a) = P· a+ T · m

(5)

Finally, the function for emotion decay Ω

is de-

ﬁned as a 6-dimensional vector. This vector con-

tains the amount of decrement or increment desired

for each of the six basic emotions. In our case, we use

0.03 to increase the intensity of an emotion or -0.03

to decrease its intensity. The mood state is updated by

a function that calculates the mood change based on

the new emotional state:

t+1

= m

+ Ψ

(p, ω

t+1

, σ

, a) + Ω

(p, ω

t+1

, σ

)

(6)

We use a 3 × 6 Emotion-Mood Inﬂuence Matrix

Q, which deﬁnes the relation between emotions and

each mood dimension. The inﬂuence of vector a on

the mood is calculated by Q×a. Similarly to the emo-

tion update, using the personality, we now also deﬁne

a 3 × 10 Personality-Mood Inﬂuence Matrix R (how

personality inﬂuences each mood dimension). This

matrix is also deﬁned once and it is multiplied with

the vector p to obtain a new vector v. We use this new

vector to construct a diagonal matrix R. So, we give

the following deﬁnition for the function Ψ

(p, σ

, a) = R

· Q· a (7)

Finally, the function for emotion decay Ω

is de-

ﬁned as a 3-dimensional vector. This vector con-

tains the amount of increment or decrement desired

for each of the three moods. In our case, the desired

values to increase the good mood normally are 0.03

for the good value, 0.1 for the neutral value, and -0.3

for the bad value. The desired values to increase the

bad mood are -0.03 for the good value, 0.1 for the

neutral value, and 0.3 for the bad value.

3.3 EBDI-based Action Selection

We use an EBDI (Emotion-Belief-Desire-Intention)

architecture similar to those presented in (Georgeff

et al., 1999) (Jiang et al., 2007) (Pereira et al., 2008),

but we add time constraints to choose the actions a

virtual human should perform according to external

and internal events, and how these change virtual hu-

man’s affective state considering time. For this mat-

ter, we use Event Calculus, which is a temporal for-

malism that allows reasoning about events and the

time when these occur (Kowalski and Sergot, 1986).

We selected this formalism by its intuitive deﬁnition

of events. Next, we present the list of the event calcu-

lus predicates used to formalized our approach:

• Initiates(e(), f, t): Fluent f holds, after event e() is

perceived at time t.

• Terminates(e(), f, t): Fluent f does not hold, after

event e() is perceived at time t.

• HoldsAt(f, t): Fluent f holds at time t.

• Happens(e(), t): Event e() is perceived at time t.

• InitiallyP(f): Fluent f holds from t = 0.

For a detailed deﬁnition of the axioms that rule

these predicates see (Shanahan, 1999).

A ﬂuent is a variable that can change over time.

We use boolean ﬂuents. We consider the following

three sets of boolean ﬂuents:

1. A set of emotions, which consists of the six emo-

tions: anger (an), disgust (di), fear (fe), happiness

(ha), sadness (sa), and surprise (su);

2. A set of beliefs, which are agents’ assumptions

about the state of the world and agents possible

skills;

3. A set of desires, which are agents’ goals.

The above is formalized as follows:

E = {an, di, fe, ha, sa, su} (8)

B = {b

, b

, ··· , b

} (9)

D =



, d

, ··· , d



(10)

Fluents = E ∪B∪D (11)

We assume that agents have a set of initial beliefs

(IB) and initial desires (ID), which are treated in the

following way:

IB ⊆ B, ∀ f ∈ IB : InitiallyP( f) (12)

ID ⊆ D, ∀f ∈ ID : InitiallyP( f) (13)

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108

The set of intentions I =

{intention

, intention

, ··· , intention

} represents all

the possible plans agents select in order to follow a

given course of action to achieve a goal, according to

agents’ emotional state and beliefs.

An intention is deﬁned as follows:

intention = {Happens(e

(),t

) ∧Happens(e

(),t

)

∧·· · ∧ Happens(e

(),t

) ∧ t

< t

< · ·· < t

} (14)

The events that happen in the virtual humans’ en-

vironment can be either internal events (IE) and ex-

ternal events (EE); internal events when they refers

to actions perform by virtual humans in the achieve-

ment of goals; and external events when the virtual

human perceives a change in the environment. These

are denoted as follows:

IE = {ie

(), ie

(), ·· · , ie

()} (15)

EE = {ee

(), ee

(), ·· · , ee

()} (16)

Events = IE ∪ EE (17)

In addition, we consider that agent’s beliefs with

respect to the actions it can perform, cannot be ac-

complished, for example, an agent believes that it can

swim in certain conditions, however when it attempts

swimming, it realizes that such belief is not truth, and

then its beliefs must be updated. This update process

is achieved in the following way:

HoldsAt(b

,t) ∧ HoldsAt(b

,t)

∧··· ∧ HoldsAt(b

,t)

∧Initiates(e(), d

,t)

← Happens(e(),t) ∧ HoldsAt(emotion

,t)

∧HoldsAt(emotion

,t)

∧··· ∧ HoldsAt(emotion

,t) (18)

Happens(e

(),t

) ∧ Happens(e

(),t

)

∧··· ∧ Happens(e

(),t

)

∧t

< t

< ·· · < t

← HoldsAt(d

,t) (19)

Happens(e

(),t

) ∧ Happens(e

(),t

)

∧··· ∧ Happens(e

(),t

) ∧ t

< t

< ·· · < t

∧Terminates(e

fails(), b

)

← Happens(e

fails(), t

)

∧HoldsAt(emotion

)

∧HoldsAt(emotion

)

∧··· ∧ HoldsAt(emotion

) (20)

Terminates(e

(), d

) ← Happens(e

(),t

) (21)

Where, emotion

∈ E, b

∈ B, e

() ∈ Events, and

∈ D.

In equation 18, certain emotions hold and an event

is perceived at time t; then a certain desire d

is initi-

ated if a set of beliefs holds at the same time t. The

initiation of desire d

activates a set of possible course

of actions in order to achieve d

, this is expressed in

equation 19. Next, if there is failure in the execu-

tion of the plan (denoted by Happens(e

fails(), t

)),

then another plan is selected according to the current

predominant emotion emotion

(see equation 20).

Now, whenever the last action of any plan is success-

fully performed, desire d

is removed from agent’s de-

sires, because it was achieved (see equation 21).

4 CASE STUDY

We present as case study a situation where there are

two virtual humans, a woman and a man interacting in

a virtual environment. Woman has a hysteric person-

ality and man has a psychopathic personality. Figure

3 shows the study case. We assume that the virtual

humans have the following beliefs and desires:

• Male virtual human’s beliefs: IamASilentWalker

and WomanIsDistracted.

• Female virtual human’s beliefs: IamCalm and

IHaveNothingToDo.

In the male virtual human side, we have the fol-

lowing plan deﬁnition:

HoldsAt(WomanIsDistracted, t)

∧HoldsAt(IamASilentWalker, t)

∧Initiates(WalkToWoman(), ScareWoman,t)

← Happens(WalkToWoman(),t)

∧HoldsAt(happiness,t) (22)

Once the male virtual human adopts a desire, it

computes a plan to release the desire (see ﬁgure 3.a,

3.b and 3.c).

Happens(WalkToWoman, t

)

∧Happens(ScreamToWoman, t

) ∧t

< t

← HoldsAt(ScareWoman, t) (23)

Now, in the female virtual human side (see ﬁgure

3.a, 3.b and 3.c), the behavior is speciﬁed as follows:

A COGNITIVE MODEL FOR HUMAN BEHAVIOR SIMULATION IN EBDI VIRTUAL HUMANS

109

Figure 3: Exposed behavior by virtual humans according to

their personality and the events they perceive.

HoldsAt(IhaveNothingToDo, t)

∧HoldsAt(IamCalm, t)

∧Initiates(Wandering(), FeelTheWind, t)

← Happens(Wandering(),t)

∧HoldsAt(Happiness,t) (24)

Once the male virtual human frustrates female vir-

tual human desires of feel the wind, the female creates

a new plan and updates some beliefs (see ﬁgure 3.d).

This is shown next:

Happens(Jump(),t

) ∧ Happens(Scream(),t

)

∧Happens(GetAway(), t

) ∧ t

< t

∧Terminates(MaleScareHer(), IamCalm, t)

← Happens(MaleScareHer(), t)

∧HoldsAt(Happiness,t) (25)

Then, the male reacts according to the events per-

ceived (see ﬁgure 3.d, 3.e and 3.f):

Happens(LaughtAtHer(), t

)

∧Happens(SayGoodByeHer(),t

) ∧ t

< t

∧Terminates(GetAway(),WomanIsDistracted, t)

← Happens(GetAway(),t)

∧HoldsAt(Happiness,t) (26)

This simple scenario showed how simple is to

model virtual human behavior based on EBDI by us-

ing event calculus.

5 CONCLUSIONS

In this paper, we presented a behavior model for re-

alistic virtual humans. This model is valid from a

psychological point of view, because it is supported

by studies on Personality and the resources provided

by them. With the use and interpretation of MMPI,

we implemented a real behavior model for virtual hu-

mans, because we do not simulate predeﬁned actions,

but we consider all possible reactions for each person-

ality scale deﬁned by MMPI, according to perceived

events from the environment. With this, virtual hu-

mans are able to behave in different ways to the same

perceived events based on their personality and affec-

tive state. We also have implemented an EBDI-based

intention selection using the Event Calculus formal-

ism. This intention selection mechanism allows vir-

tual humans to perform actions based on their current

emotional state, beliefs, desires and intentions. Thus,

these intentions are used to deﬁne virtual humans’ be-

havior for each situation they experience in the envi-

ronment according to their personality and the events

they perceive.

6 FUTURE WORK

As continuation of our work we are creating and im-

plementing a complete fuzzy model that will be used

to update virtual humans’ mood and emotional state

in a more natural and efﬁcient way. We are work-

ing on deﬁning different fuzzy sets and fuzzy rules

to update and regulate virtual humans’ affective state

according to their personality, emotional history and

mood history, taking into account the level of inten-

sity of perceived events from their environment. With

this new fuzzy model, we will completely replace the

matrix model that was described above. Finally, we

also are working on applying the principles of Emo-

tional Intelligence Model (Goleman, 1995) to endow

virtual humans with an emotional intelligence that al-

lows them to perceive and express emotions, assimi-

late emotion-related feelings, understand and reason

with emotion, and regulate emotions in themselves

and other virtual entities. To implement this intelli-

gence, we intent to use the Emotional Competence

Framework (Mayer et al., 2000) deﬁned in the Emo-

tional Intelligence Model. With this framework, we

will develop virtual humans’ personal and social com-

petencies on the basis of the following characteristics:

Personal Competencies such as self-consciousness,

self-regulation and self-motivation, and Social Com-

petencies such as social awareness and social skills.

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ACKNOWLEDGEMENTS

This research is partially supported by CoECyT-Jal

project no. 2008-05-97094, whilst authors H´ector

Orozco, Victor Fern´andez and Octavio Guti´errez are

supported by CONACYT grants no. 203060, 212753,

and 191493, respectively.

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