An Architecture for Autonomous Normative BDI Agents based on
Personality Traits to Solve Normative Conflicts
Paulo Henrique Cardoso Alves, Marx Leles Viana and Carlos José Pereira de Lucena
Laboratory of Software Engineering (LES), Department of Computer Science,
Pontifical Catholic University of Rio de Janeiro PUC Rio, Rio de Janeiro, RJ, Brazil
Keywords: Solving Normative Conflicts, Normative Agents, Multiagent Systems, Personality Traits.
Abstract: Norms are promising mechanisms of social control to ensure a desirable social order in open multiagent
systems. Normative multiagent systems offer the ability to integrate social and individual factors to provide
increased levels of fidelity with respect to modelling social phenomena such as cooperation; coordination;
decision-making process, and organization in artificial agent systems. However, norms eventually can be
conflicting for example, when there is a norm that prohibits an agent to perform a particular action and
another norm that obligates the same agent to perform the same action, the agent is not able to fulfill both
norms at the same time. The agent’s decision about which norms to fulfill can be defined based on rewards,
punishments and agent’s goals. Sometimes, the analysis between these attributes will not be enough to allow
the agent to make the best decision. This paper introduces an architecture that considers the agent’s personality
traits in order to improve the normative conflict solving process. In addition, the agent can execute different
behaviors with equal environment variables, just by changing its own internal characteristics. The
applicability and validation of our approach are demonstrated by an experiment that reinforces the importance
of the society’s norms.
Multiagent Systems (MASs) are societies in which
these heterogeneous and individually designed
entities (agents) work to accomplish common or
independent goals (Viana et al., 2016). In order to
deal with autonomy and diversity of interests among
the different members, such systems provide a set of
norms, which are mechanisms used to restrict the
behavior of agents by defining what actions to which
the agents are: (i) obligated (agents must accomplish
a specific outcome); (ii) permitted (agents can act in
a particular way) or (iii) prohibited (agents must not
act in a specific way) all to encourage the fulfillment
of the norm through rewards definition and
discouragement of norm violation by pointing out the
punishments (Figueiredo et al, 2010).
Norms must be complied with by a set of agents
and include normative goals that must be satisfied by
the addressees. In addition, norms are not always
applicable, and their activation depends on the
environment in which agents are situated. In some
cases, norms suggest the existence of a set of
sanctions to be imposed when agents fulfill, or
violate, the normative goal.
The decision-making process about which norms
will be fulfilled or violated might be defined based on
the agent’s goals, rewards and punishment analysis
(Viana et al., 2016). Since an agents priority is the
satisfaction of its own goals, before complying with
the norms the agent must evaluate their positive and
negative effects on its goals (Lo
pez and Ma
2004) without hurting the agent’s autonomy. Both
rewards and punishments are the means by which the
agents know what might happen independently of the
agent’s decision to comply, or not, with the norms.
However, norms sometimes may conflict or be
inconsistent with one another (Mccrae and John,
1992). For instance, different norms can, at the same
time, prohibit and obligate a state that the agent wants
to fulfill and the simple balance between goals,
rewards and punishments might not be enough to
permit the agent to make the best decision.
The abstract normative agent architecture
developed by (Lo
pez and Ma
rquez, 2004), has four
main steps: (i) agent perception, i.e., when the agent’s
beliefs and a set of norms are updated; (ii) norm
Alves, P., Viana, M. and Lucena, C.
An Architecture for Autonomous Normative BDI Agents based on Personality Traits to Solve Normative Conflicts.
DOI: 10.5220/0006599300800090
In Proceedings of the 10th International Conference on Agents and Artificial Intelligence (ICAART 2018) - Volume 1, pages 80-90
ISBN: 978-989-758-275-2
Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
adoption, i.e., when agents verify which norms are
addressed to them; (iii) norm deliberation, i.e., when
agents verify which norms they intend to fulfill, or
violate, and (iv) norm compliance, i.e., when agents
verify which norms they will comply with. Within the
norm deliberation step, conflicting norms are verified
and a set of these norms is added to the norm
compliance set.
We changed the internal process of the norm
deliberation step to deal with conflicting norms by
adding the agent’s personality traits. These
characteristics will help the software agents make
some different decisions involving personality traits
based on the OCEAN model (Mccrae and John, 1992),
setting a weight for each one of these characteristics. We
will present an experiment comparing different
approaches to deal with normative conflicts based on
social profiles and personality traits. This will illustrate
the new deliberation process proposed in this paper.
Within this context, we present an approach that
builds BDI agents with personality traits (Barbosa et al.,
2015) to improve the decision-making process for the
solution of normative conflicts. This approach aims at
offering new resources for the agent to deal with
conflicting norms supported by personality traits. As
such, more human characteristics can be considered
in order to improve the deliberation process. By using
these new functions, it is possible to build agents that:
(i) use personality traits to improve the solution
between normative conflicts, and (ii) evaluate the
effects on its desires with respect to the fulfilment, or
violation, of a norm and thus use all of these functions
to conduct experiments to learn how different
strategies could change an agent’s behavior.
The paper is structured as follows: Section 2
focuses on the background, while Section 3 discusses
related work. Section 4 presents the BDI-agent
approach to personality traits to solve normative
conflicts. Section 5 presents the experiment that
evaluates our approach. Finally, Section 6 shows our
conclusion and future work.
This section describes the main concepts related to
agents and multiagent systems. First, we will discuss
norms and BDI (Belief-Desire-Intention)
architecture. We will also discuss the relation
between normative conflicts.
2.1 Norms
Norms are designed to regulate the behavior of the
agent, and therefore, a norm definition should include
the address of the agent being regulated (Bordini et
al., 2007). However, norms are different from laws,
and they cannot force agents to comply with them.
Agents are autonomous entities, so norms can only
suggest and present the expected behavior to which
the agent will decide to comply with, or not.
In this work, we used the norm representation
described in (Viana et al., 2015) . Norms properties
are briefly described in Table 1. For example, the
property Addressee is used to specify the agents or
roles responsible for fulfilling the norm.
Table 1: Norm Description.
It is the agent or role responsible
for fulfilling the norm
It is the condition for the norm to
become active
It is the condition for the norm to
become inactive
It represents the set of rewards to
be given to the agent to fulfill a
It is the set of punishments to be
given to the agent for violating a
It indicates if the norm states an
obligation, a permission or a
It describes the set of states being
In order to better understand the application of
norms to regulate agents with a different social
profile, we made a comparison between the social
contribution and the individual satisfaction of the
agent for fulfilling, or violating, the norms for each
approach. Furthermore, to better understand the
definition of norms and their representation, imagine
a user scenario where the employee agent has to
decide the transportation type to go home. The
agent’s goal is to increase physical conditioning and
has the following options to go home: (i) by bicycle,
which is a way to satisfy the agent’s goal, and (ii) by
bus, if it is raining, in which case, the agent cannot
accomplish its goal at this time.
In addition, each employee agent should decide
according to specific norms. Eventually, a norm is
sent to each employee agent with the following
statement: “go home by bus, it is raining”. This norm
has the following attributes: (i) addressees are
employee agents; (ii) the required deontic concept is
prohibition, because it prohibits the agent to go home
by bicycle, and (iii) when an agent agrees to a norm,
An Architecture for Autonomous Normative BDI Agents based on Personality Traits to Solve Normative Conflicts
it will receive a reward. In this case, the reward may
be not getting the flu. If the employee agent violates
the norm, the agent will receive the punishment
associated with the norm. For example, when it is
raining and the employee agent really wants to work
out, it then will violate the norm by going home by
bicycle, which will result in the decrease of the
agent’s health, because the agent will probably come
down with the flu. In this case, a punishment
associated with the norm will be applied to the agent,
i.e., the agent cannot work the next day because it is
sick. Note that the norm is activated when it is raining.
In turn, the norm expires when the weather is sunny.
2.2 Conflicting Norms
Norms eventually may conflict, i.e., an action may be
simultaneously prohibited and permitted, or it may be
inconsistent, i.e., when an action is simultaneously
prohibited and obligated (Vasconcelos et al., 2007).
These conflicts and inconsistencies may be caused by a
norm that prohibits an agent to perform a particular
action while another norm obligates the same agent to
perform the same action at the same time. The agent can
realize any action in the environment until an active
norm restricts its goals. For example, Figure 1 presents
a scenario of conflicting norms when a norm
defines that the buyer agent cannot bring back the
product bought and at the same time another norm
defines that the buyer agent can return the product
bought before opening it.
Figure 1: Conflict - Prohibition and Permission.
Figure 2 presents another scenario of conflicting
norms the seller agent can only re-price the
products before the store opens and another norm
permits the seller agent to re-price them when the
store is open and there is a sale.
Figure 2: Conflict - Permission and Obligation.
In short, conflicts may occur in different cases and
situations, and dealing with them is extremely
important to make the best decision.
2.3 BDI Architecture
The BDI (Belief-Desire-Intention) model was
proposed by (Bratman, 1987) as a philosophical
theory of practical reasoning, representing, the
information, the motivational and deliberative states
of the agent, respectively. There are two main steps:
(i) applying a filter to make a set of goals to which the
agent must commit to serve as the basis of its beliefs,
and (ii) finding a way to understand how the desires
produced can be fulfilled based on the agent’s
available resources (Wooldridge and Ciancarini,
Figure 3: Generic BDI architecture (Wooldridge et al.,
Figure 3 shows the BDI model, which is
composed of three mental states: (i) beliefs, which
represent the environment factors that are updated
after each perceived action they represent the
agent’s world knowledge; (ii) desires, which have
information about the goals to be fulfilled they
represent the agent’s motivational state, and (iii)
intentions, which represent the action plan chosen.
The BDI architecture starts with a Belief Revision
Function that makes a new belief set based on the agent’s
perception. Next, the Option Generation Function
sets the agent’s available options and desires, based
on its own environment beliefs and intentions. The
next function is a Filter that sets the agent’s intentions
based on its own beliefs, desires and intentions.
Finally, the Action Selection Function sets the actions
to be executed based on the current intentions.
Most BDI systems are inspired by the Rao and
Georgeff (Rao and Georgeff, 1995) model. The
authors presented an abstract BDI interpreter. This
interpreter works with beliefs, goals and agent plans.
As such, the goals are a set of concrete desires that
may be evaluated all together, avoiding a complex
goal deliberation step. The interpreter’s main
functionality is the means to the end process,
achieved by plan selection and plan execution given
a goal or event.
ICAART 2018 - 10th International Conference on Agents and Artificial Intelligence
2.4 Personality Traits
The big-5 model (Mccrae and John, 2011), also
known as OCEAN model, provides a mechanism to
define personality traits based on such concepts and
defines five factors: (i) Openness, describing a
dimension of personality that portrays the
imaginative, creative aspect of the human character,
(ii) Conscientiousness, determining how much an
individual is organized and careful, (iii) Extroversion,
related to how outgoing and sociable a person is, (iv)
Agreeableness, which is about friendliness,
generosity and the tendency to get along with other
people, and (v) Neuroticism, referring to emotional
instability and the tendency to experience negative
Each factor is composed of many traits, which
basically are used to describe people (Mccrae and
John, 2011) (Goldberg, 1990). The factors presented
will be used to help the agent’s decision-making
process and plan selection, according to the agent’s
individual goals and intended norms.
Based on the OCEAN model, the personality traits
may be built through the distribution of weights
between the factors: (i) Openness to experience; (ii)
Conscientiousness; (iii) Extroversion; (iv)
Agreeableness, and (v) Neuroticism. In Figure 4,
agent 1 may be creative and adventurous, while agent
2 may be careful.
Figure 4: OCEAN model application example.
This section describes some related work: (i) the
solution for normative conflicts (López, 2003),
(Criado et al., 2010), (Neto et al., 2011); (ii)
architecture designs considering the agent’s
emotional state (Pereira et al., 2005), and (iii) the
agent’s personality (Barbosa et al., 2015), (Jones et
al., 2009).
Pereira et al. (Pereira et al., 2005) proposed an
architecture based on the BDI (Belief-Desire-
Intention) model to support artificial emotions,
including internal representations of the agent’s
capabilities and resources. This research introduces
subjects, such as artificial emotions, agent means and
BDI architecture. Furthermore, a common-sense
definition of new mental states, such as emotions, was
developed, and influenced the BDI architecture
through the common-sense understanding of the way
they positively affect human reasoning. The authors
defined a new concept: Fear, an informational data
structure that reports situations which an agent should
avoid. This work presents the Personality Traits in
BDI-Agent architecture as an extended version of the
classic BDI. However, the authors do not compare the
results with other approaches that may, or may not,
apply emotions and neither provide support to solve
normative conflicts (Pereira et al., 2005).
The authors in (Barbosa et al., 2015) built a
decision process to work as part of the story-telling
systems wherein narrative plots emerge from the
acting characters’ behaviors and personality traits.
The process evaluates goals and plans to examine the
plan commitment issue. The drives, attitudes and
emotions play a major role in the process. However,
the personality traits were not applied to MASs,
which creates an opportunity to improve the agent’s
decision-making process to deal with normative
Jones et al. (Jones et al., 2009) developed a BDI
extension to consider physiology, emotions, and
personality. It is used to model crisis situations such
as terrorist attacks, for instance. The emotions were
used in pairs such as fear/ hope, anger/gratitude and
shame/pride. The physiology may be affected by the
simulation environment and may change the agent’s
health. The following characteristics were
considered: stress, hunger/thirst, temperature, fatigue,
injuries and contamination. The personality is a set of
characteristics that determines that agents are
psychologically, mentally and ethically different
from each other. However, this approach was not
applied in Normative Multiagent Systems to evaluate
different behaviors that may emerge with personality
traits applications.
Some approaches (López, 2003), (Criado et al.,
2010), (Neto et al., 2011) have been proposed in the
literature to develop agents that evaluate the effects
of solving normative conflicts. For instance, the
n-BDI architecture defined by Criado et al. (Criado et
An Architecture for Autonomous Normative BDI Agents based on Personality Traits to Solve Normative Conflicts
al., 2010) presents a model for building environments
governed by norms. Basically, the architecture selects
objectives to be performed based on the priority
associated with each objective. An objective’s
priority is determined by the priority of the norms
governing a specific objective. However, it is not
clear in this approach how the properties of a norm
can be evaluated. In addition, the approach neither
supports a strategy nor considers the agent’s
personality traits to deal with conflicts between
Lopez et al. (López, 2003) defined a set of
strategies that can be adopted by agents to deal with
norms, as follows: Pressured, Rebellious and Social.
For example, the Pressured strategy occurs when
agents fulfill the norms to achieve their individual
goals considering only the punishments that will harm
them. Another is the Rebellious strategy, in which
agents consider only their individual goals and violate
all the environment’s norms. Finally, the Social
strategy happens when agents first of all comply with
norms and after verify if it is possible to fulfill some
individual goals. Although this work provides some
mechanisms for the agents to collect norms, the
authors do not provide a framework that can be
extended to create simulations of normative
multiagent systems by including new strategies. In
addition, this work can neither extend mechanisms to
collect information during the simulations nor extend
mechanisms to generate norms and agent goals.
Furthermore, the agent cannot detect and overcome
normative conflicts.
Finally, Santos Neto et al. (Neto et al., 2011)
propose the NBDI architecture, based on the Criado
et al. (Criado et al., 2010) research, to develop
goal-oriented normative agents whose priority is the
accomplishment of their own desires while evaluating
the pros and cons associated with the fulfillment or
violation of the norms. To make this possible, the BDI
architecture was extended by including norms-related
functions to check incoming perceptions, and select
norms based on the agent’s desires and intentions. A
detection conflict and a solving conflict algorithm
were developed based on norms contributions; in the
case of conflicts between norms, the one with the
highest contribution to the achievement of the agent’s
desires and intentions can be selected. If the norm
contributions have equal values, then the first norm
will be selected. Therefore, as it is possible to
observe, sometimes the norm contribution is not
enough for the agent to make a better decision. We
identified this gap and improved the decision-making
process, adding the personality traits concept.
As none of this related work deals with norm
conflicts using personality traits, this was the gap that
we based on to propose our work. We aim at
providing a better way to balance goals, rewards,
punishment and personality traits to solve normative
conflicts and improve the deliberation process. To
evaluate the norm contribution, we first use rewards
and punishment values. With these values, we then
continue to evaluate the norm contribution, now
adding personality traits.
This section describes the main concepts required to
understand the approach based on BDI agents with
personality traits. This architecture improves the
solution of normative conflicts and, after helping the
deliberation process, it deals with non-conflicting
norms and agent goals. In addition, we provide a
software framework overview and discuss its
different components.
4.1 The Architecture
The Personality Traits in BDI agents approach that
can solve the normative conflicts were inspired on the
concepts presented in the background and the related
work sections.
Figure 5: The architecture.
We added both BDI features and personality traits
in the conflicts resolution and normative deliberation
process. The architecture foundation was based on the
abstract normative agent architecture developed in
pez and Ma
rquez, 2004). Figure 5 presents our
BDI agent with personality traits architecture to solve
normative conflicts.
The most significant change was adding to the
deliberation process a reasoning step that involves the
BDI architecture and the personality traits approach.
Both strategies work in a complementary way to
ICAART 2018 - 10th International Conference on Agents and Artificial Intelligence
change the agent’s behavior, considering factors that
were not used in the norms deliberation process in
previous work. All of these changes refer only to the
internal agent process. The decision-making process
proposed has four steps, which are described below.
The first step involves the agent’s perception in
the Belief Revision Function, where the agent
perceives the active norms in the environment
addressed to it by means of its sensors. Then, the
agent inserts into the Norms set the norms that it
wants to fulfill by using the Norms Adoption
function. After that, the agent updates its beliefs,
taking into account these new norms.
The second step is the Desire Normative
Generator, which is composed of three processes: (i)
Norm Status Evaluation function, where the agent
verifies which norms are activated or deactivated; (ii)
Norms Conflict Detection function, where the agent
verifies what the normative conflicts are, and (iii)
Solution Normative Conflicts function, where the
agent evaluates the norms contribution and solves the
normative conflicts, also considering its personality
traits based on the OCEAN model. Table 2 shows
some examples of personality traits composition that
we consider: drives, attitudes and emotions, as in
(Mccrae and John, 1992). Our personality traits
model has only two properties: (i) a name and (ii) a
value indicating its weight.
Table 2: Personality Traits Examples.
Sense of duty
Material gain
The norms analyses are based on the normative
contribution, which is composed by the evaluation
between rewards, punishments, goals and personality
traits. Figure 6 shows the normative contribution
Figure 6: Normative Contribution Equation.
The normative contribution concept was extended
from (Neto, 2011). We added the goals and
personality traits weights. The m bound refers to the
summation of the activated norms addressed to the
agent; the p bound refers to the summation of the
agent’s goals, and q refers to the summation of the
agent’s personality traits. Table 3 describes the goal
Table 3: Goal Properties.
It is the name of the goal
It is the value that represents the
importance of this goal
Norm Required
It is the set of the norms required
to permit that the goal be
It is the set of the personality traits
required to permit that the goal be
Belief Required
It is the set of the beliefs required
to permit that the goal be
The personality traits are used only in two
situations: (i) at Solution Normative Conflicts
through the equation shown in Figure 6 and (ii) at the
plan selection step. Table 4 describes the plan
properties. A set of non-conflicting norms is exported
to the next step. The goals that are not restricted by
the norms are the agent’s Desires.
Table 4: Plan Properties.
It is the name of the goal
It is the value that represents the
importance of this plan
It is the set of the personality traits
that contribute to this plan
Goal Required
It is the set of the goals required to
permit that the plan be activated
The third step is the Normative Filter, which is
composed of two processes taking into account the
agent’s personality traits: (i) Norms Evaluation
function, where the agent evaluates the Desires set
and it decides which norms will be fulfilled, and (ii)
Plan Selection function, where the agent will choose
its best plans in the Intentions set.
Finally, the fourth step is the Action Selection
function, which is composed of the Normative
executor and selector. This function receives the
Norms set, which are the norms that the agent intends
to fulfill. Last but not least, all of these steps help to
improve the normative conflict solving process,
considering personality traits inserts into the BDI
reasoning process.
An Architecture for Autonomous Normative BDI Agents based on Personality Traits to Solve Normative Conflicts
4.2 The Framework
Inspired by the JSAN architecture (Viana et al.,
2015), which uses different normative strategies to
deal with norms and takes into account the different
agent’s social levels, as in (López, 2003), we built a
new approach by introducing personality traits
aiming to improve the solution of the normative
conflict. Our framework provides the
decision-making process described in Section 4.1.
Figure 7 shows the framework architecture.
The Normative BDI Agent class is composed of
goals, role, norms, beliefs, desires, intentions, and
personality traits. By using these attributes, the agent
starts the decision-making process to solve normative
conflicts. In the normative conflict solving process,
the agent will choose the norms that it will add to the
Intentions set and finally will decide which norms
will be fulfilled according to the agent’s social
profile, as in (Bordini et al., 2007) and (López, 2003).
The solving process of normative conflicts starts
with the calculation of each norm’s normative
contribution, wherein the agent evaluates its rewards
and punishments and compares each normative
contribution with other norms addressed to it.
Furthermore, we added a new step to improve this
process, also taking into consideration the agent’s
goals and its personality traits. This new step consists
of the choice of the normative goals that can be
fulfilled according to the agent’s goals and its
personality traits.
Figure 7: The Framework architecture.
The agent will verify which goal can be fulfilled
based on its personality traits, so the agent uses its set
of goals and analyzes each conflicting norm, adding
to the normative contribution an integer value to
represent the compatibility between the agent’s goals
and the normative goals. The compatibility is defined
by the evaluation of which of the agent’s goals can be
achieved if a norm is fulfilled. As a result, some
conflicting norms may have changed their normative
contribution based on the use of the agent’s
personality traits. For instance, imagine one norm that
obligates an agent to cross a damaged bridge. If the
agent is careful (careful meaning the agent's
personality trait) its normative contribution will be
decreased because the agent does not have the intent
to cross a damaged bridge it is dangerous.
Our initial experimentation includes different kinds
of agents to deal with norms, such as described in
pez and Ma
rquez, 2004) and (Neto et al., 2011).
The (Lo
pez and Ma
rquez, 2004) approaches deal with
norms considering the following strategies: (i) Social,
i.e., the agent fulfills all of the active norms addressed
to it and then it verifies which goals can be fulfilled;
if there are conflicts, it randomly selects one norm
from each conflicting norms set to be complied with,
(ii) Rebellious, i.e., the agent violates all norms and
fulfills all goals, and in this case it does not matter if
there are conflicting norms; the agent will never
fulfill any norms, and (iii) Pressured, i.e., the agent
only fulfills the norms whose normative punishment
value is bigger than the value of the importance of the
goals; thus the agent feels pressured to comply with
the norm to avoid punishments. In (Neto et al., 2011)
the authors present the NBDI approach, which
considers the normative contribution generated by
evaluation between: (i) the norms’ rewards and
punishments, and (ii) the importance of the goals.
We chose these examples to compare with our
approach because they represent the most common
strategies followed by agents when they face a norm
compliance decision. Our approach is based on (Neto
et al., 2011) and was improved by adding personality
traits. A normative conflict is identified when
different norms are active and have opposite deontic
concepts. The norm contribution is then evaluated for
each one of the conflicting norms and there are a few
steps to follow: (i) for each goal, its importance is
increased by a weight given to a personality trait, (ii)
for each goal allowed by a norm (the norm does not
restrict this goal), the norm contribution is increased,
adding the importance of the goal, and (iii) for each
norm that is active at the same time and has opposite
deontic concepts, the norm with the better norm
contribution value is selected.
For the non-conflicting norms (i) a set of norms
indexed by the goals that the norm restricts is created,
ICAART 2018 - 10th International Conference on Agents and Artificial Intelligence
(ii) for each non-conflicting norm, the norm contribution
is increased adding the norm contribution value to
each norm in this set that restricts the same goal, (iii)
the norms contribution and goals increased by
personality traits are evaluated, and (iv) the better
value is selected and this norm or goal is selected to
be fulfilled. Our interest here is the observation of
how both the social contribution and the agent’s
individual satisfaction change, according to the norm
compliance strategy it chooses, the increase in the
number of conflicts between the norms it has to play
with and its personal goals. The social contribution of an
agent is defined by the number of times the agent has
fulfilled the norms addressed to it. The agent’s
individual satisfaction is the number of goals
achieved in relation to the number of goals generated.
We reproduced the experiment created in (Lo
and Ma
rquez, 2004) using all of these different
approaches and comparing them with our approach.
First, a base of goals to represent all the goals that an
agent might have is randomly created. Second, a
motivation value is associated to each goal in this set to
represent their importance. In addition, each goal might
have a personality trait associated, meaning that if
there is an agent that has this personality trait, this
goal will be increased by the personality trait value.
Both punishments and rewards in each norm are also
randomly generated, as well as the deontic concept
and activation time. Thus, the norms are evaluated by
agents following different strategies so that similar
inputs produce different outcomes.
We observed both the social contribution and the
agent’s individual satisfaction taking into account the
different percentages of normative conflicts over a
period of time. First, no conflicts were considered,
meaning that all norms and goals could be fulfilled.
Then the experiment was repeated, with the number of
conflicts increased in a proportion of 25% until all norms
conflicted among themselves. Each experiment
consisted of 100 runs, and in each run, 10 goals and
10 norms were used.
Table 5 and Table 6 show the properties of
random norms and random goals used in this
experiment, respectively.
Table 5: Random Norm Properties.
Agent “X”
All norms are activated
There is no expiration
Random value in the range [0,5]
Random value in the range [0,5] +
Set of goals restricted by this norm
Random value in the range [-1,1],
where -1 represents a prohibition, 0
represents a permission and 1
represents an obligation
Table 6: Random Goal Properties.
Goal + random value in the range
Random value in the range [0,5]
Norm Required
Random set of norms
Two personality traits with a
random value [5,10]
Belief Required
No belief was required
First, the Pressured strategy shows that the agent
achieves more individual goals rather than
contributes to the society. Figure 8 shows the agent’s
behavior in different conflicting norm situations.
Figure 8: Pressured Strategy.
The Social strategy shows that initially, with
no-conflicting norms, the agent fulfills all norms
because, first, the agent complies with all the adopted
active norms and then decides which goals will be
achieved. Figure 9 shows that, as a result, the number
of goals achieved increases gradually.
Figure 9: Social Strategy.
Agents using the Rebellious strategy violate all
norms and, as no goal are restricted, all of them will
0 2 5 5 0 7 5 1 0 0
IndividualSatisfaction SocialContribution
An Architecture for Autonomous Normative BDI Agents based on Personality Traits to Solve Normative Conflicts
be achieved. Figure 10 shows this behavior. It is
important to notice that the rewards and the
punishment values are not taken into account. In this
situation, the agent always will receive a punishment
for violating norms that restrict its goals.
Figure 10: Rebellious Strategy.
The agent using the NBDI strategy considers the
value of the social contribution to fulfill, or violate,
each norm before deciding to comply with it, or not.
Figure 11 shows that more goals are achieved when
the normative conflicts increase.
Figure 11: NBDI Strategy.
The Personality traits strategy considers the norm
contribution developed in NBDI adding the
personality traits value. The experiment results are
similar to the NBDI strategy, although the agent
meets more individual goals. Figure 12 shows the
agent’s behavior regarding norms compliance and
goals achievement.
Figure 12: Personality Traits Strategy.
As can be observed, the personality traits strategy
encourages the agent to fulfill its goals and, if there is
a personality trait with a null value, the performance
will be the same as presented by NBDI. The greater
the weight of the personality traits, the higher the
number of individual goals.
Figure 13 shows the comparison between all of
the five strategies. As a result, the Personality Traits
strategy achieved more goals than the Social,
Pressured and NBDI strategies. It shows that the
Personality Traits strategy helps the agent to fulfill
more individual goals and increases the individual
Figure 13: Individual satisfaction overview.
Figure 14 shows all of the five different strategies,
comparing the social contribution between them. As
can be observed, strategies that achieve more goals
comply with fewer norms; therefore, the Personality
Traits strategy fulfills fewer norms than other
strategies, except the Rebellious strategy, which
always violates all the norms. Thus, the developed
approach is a middle ground between Rebellious
strategy and NBDI strategy.
0 2 5 5 0 7 5 1 0 0
IndividualSatisfaction SocialContribution
0 2 5 50 75 10 0
IndividualSatisfaction SocialContribution
0 25 5 0 75 1 0 0
IndividualSatisfaction SocialContribution
0 2 5 5 0 7 5 1 0 0
NBDI Pressured PersonalityTraits Rebellious Social
ICAART 2018 - 10th International Conference on Agents and Artificial Intelligence
Figure 14: Social Contribution overview.
This paper proposes an approach to deal with
normative conflicts by adding personality traits
characteristics to the BDI architecture to improve the
decision-making process that will decide which
norms the agent shall fulfill. The main contributions
of this research are: (i) include personality traits in the
BDI architecture to change the solving process of
normative conflicts; (ii) implement different agent
behaviors according to different personality traits,
and (iii) make it possible to build software agents with
different behaviors. The BDI-agent with personality
traits was able to reason about the norms it would like
to fulfill, and to select the plans that met the agent’s
intention of fulfilling, or violating, such norms.
Moreover, the experiment developed showed that the
Personality Traits strategy results were similar to the
NBDI strategy, although the agent with personality
traits chooses to achieve more goals than with the
other strategies.
As future work, we are deciding on an
experimental study in order to apply fuzzy logic to
deal with changes found in the real world, such as the
chance to become sick if you stay in the rain.
Furthermore, the punishment for becoming ill is also
variable. An agent's punishment may range from
sneezing to pneumonia. The severity of the illness
could be a factor for the agent's current health state
and how fast the recovery takes place may also be part
of the agent's personality profile. So, when the agent
must decide whether to ride the bike in the rain, it
must calculate the reward (fitness gained) against the
possibility of becoming sick (may or may not get
sick) and the consequences (punishment) that could
range from very mild (sneezing) to very serious
(pneumonia). We also plan to implement this
approach in other more complex scenarios that take
personality traits into account. For example: (i) in risk
areas, where firefighters are responsible for planning
people’s evacuation, and (ii) in crime prevention,
where the police are responsible for arresting
criminals and keeping civilians safe. Last but not
least, we will apply these different strategies to
environments that have more agents, in order to
analyze their behavior and evaluate the norms
addressed to the agent, and the agent’s internal goals.
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