NBDI: AN ARCHITECTURE FOR GOAL-ORIENTED

NORMATIVE AGENTS

Baldoino F. dos S. Neto

Computer Science Department, PUC-Rio, Rio de Janeiro, Brazil

Viviane Torres da Silva

Computer Science Department, UFF, Rio de Janeiro, Brazil

Carlos J. P. de Lucena

Computer Science Department, PUC-Rio, Rio de Janeiro, Brazil

Keywords:

Autonomous Systems, Agent Platforms and Interoperability, Knowledge Representation and Reasoning.

Abstract:

Normative regulation is a mechanism of social control that regulates the behaviour of autonomous agents

indicating that such agents are permitted, obligated or prohibited to achieve something in the world, and

deﬁning rewards by stating stimulus to their fulﬁlment and punishments by discouraging their violation. Since

goal-oriented agents‘ priority is the accomplishment of their own desires, they must evaluate the pros and

cons associated with the fulﬁlment or violation of the norms before choosing to comply or not with them. In

this context, we propose an extension to the BDI architecture by including norms related functions to check

the incoming perceptions (including norms), select the norms they intend to fulﬁl based on the beneﬁts they

provide to the achievement of the agent‘s desires and intentions, and decide to cope or not with the norms while

dropping, retaining or adopting new intentions. We demonstrate the applicability of our approach through an

non-combatant evacuation scenario implemented by using the Normative Jason platform.

1 INTRODUCTION

Normative regulation is a mechanism that aims to

cope with the heterogeneity, autonomy and diversity

of interests among the different members of an open

multi-agent system establishing a set of norms that

ensures a desirable social order (Lopez-Lopez, 2003).

Such norms regulate the behaviour of the agents

by indicating that they are obligated to accomplish

something in the world (obligations) (Silva, 2008),

permitted to act in a particular way (permissions) and

prohibited from acting in a particular way (prohibi-

tions) (Silva, 2008). Moreover, norms may deﬁne re-

wards to their fulﬁlment and may state punishments

in order to discourage their violation(Silva, 2008).

In this paper we consider that agents are goal-

oriented entities that have the main purpose of achiev-

ing their desires while trying to fulﬁl the system

norms. In this context, the paper presents an ab-

stract architecture to build agents able to deal with

the norms of a society in an autonomous way.

The NBDI (Norm-Belief-Desire-Intention) architec-

ture extends the BDI (Belief-Desire-Intention) ar-

chitecture (Weiss, 1999) by including norms related

functions to support normative reasoning. The agents

built according to the proposed architecture have: (i)

a review function of norms and beliefs used to check

the incoming perceptions (including norms), (ii) a

norm selection function to select the norms they in-

tend to fulﬁl based on the beneﬁts they provide to

the achievement of the agent’s desires and intentions,

and to identify and solve conﬂicts among the selected

norms, and (iii) a norm ﬁlter where the agents decide

to cope or not with the norms while dropping, retain-

ing or adopting new intentions.

We demonstrate the applicability of the NBDI ar-

chitecture through a non-combatant evacuation sce-

nario where the tasks related to review, select and ﬁl-

ter norms are implemented by using the Normative

Jason platform (Neto et al., 2010) that already pro-

vides support to the implementation of BDI agents

and a set of normative functions able to check if the

agent should adopt or not a norm, evaluate the pros

and cons associated with the fulﬁlment or violation of

116

F. dos S. Neto B., Torres da Silva V. and J. P. de Lucena C..

NBDI: AN ARCHITECTURE FOR GOAL-ORIENTED NORMATIVE AGENTS.

DOI: 10.5220/0003179801160125

In Proceedings of the 3rd International Conference on Agents and Artiﬁcial Intelligence (ICAART-2011), pages 116-125

ISBN: 978-989-8425-40-9

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

the norm, check and solve conﬂicts among norms, and

choose desires and plans according to their decisions

of fulﬁlling or not a norm.

The paper is structured as follows. In Section 2

we outline the background about norms that is nec-

essary to follow the paper. In Section 3 we present

the non-combatant evacuation scenario where norms

are deﬁned to regulate the behaviour of rescue agents.

In 4 the proposed NBDI normative agent architecture

is explained and exempliﬁed by using the proposed

scenario. Section 5 demonstrates the applicability of

the NBDI architecture. Section 6 summarizes rele-

vant related work and, ﬁnally, Section 7 concludes

and presents some future work.

2 NORMS

In this work, we follow the approaches presented in

(Kollingbaum, 2005), where norms can specify world

states that an agent is obligated to achieve or pro-

hibited from achieving. Moreover, we consider, as

discussed in (Silva, 2008), that norms always spec-

ify a set of addressee agents or roles that are di-

rectly responsible for the adoption and fulﬁlment of

the norms.

Norms are not applied all the time, but only in par-

ticular circumstances. Thus, norms must always spec-

ify the contexts in which the addressee must adopt

and fulﬁl them. In addition, to discourage personal

interests of preventing the fulﬁlment of norms, mech-

anisms either to promote the fulﬁlment of the norm

or to inhibit deviation from them are needed. Norms

may include rewards to be given when they are ful-

ﬁlled and punishments to be applied when they are vi-

olated (Silva, 2008). Rewards can give permission to

the agent to achieve a set of states while punishments

typically obligates or prohibits the agent to achieve a

set of states.

In face of presented, we adopt the representation

for norms described in (Neto et al., 2010), as shown

below:

norm (Addressee, Activation, Expiration,

Rewards,Punishments, DeonticConcept, State)

where Addressee is the agent or role responsible

for fulﬁlling the norm, Activation is the activation

condition for the norm to become active, Expiration

is the expiration condition for the norm to become

inactive, Rewards are the rewards to be given to the

agent for fulﬁlling a norm, Punishments are the pun-

ishments to be given to the agent for violating a norm,

DeonticConcept indicates if the norm states an obli-

gation or a prohibition, and State describes a set of

states being regulated.

3 SCENARIO: RESCUE

OPERATION

The applicability of the architecture proposed in this

paper is demonstrated by using the simpliﬁed non-

combatant evacuation scenario, presented in (Bur-

nett et al., 2008). In such scenario agents have the

goals to plan the evacuation of members of a Non-

Governmental Organisation (NGO) that are in haz-

ardous location and, to do so, they can use differ-

ent resources that help to evacuate the members, such

as: (i) helicopters, (ii) troops and (iii) land-based he-

licopters. Considering that such resources are lim-

ited, we have a Commander Agent that is responsible

to control the use of the resources regulating the be-

haviour of the agents according to the norms 1, 2 and

Norm 1

Addressee: Rescue Entity

Activation: NGO workers are stranded in a haz-

ardous location

Expiration: NGO workers are stranded in a safe

location

DeonticConcept: Obligation

State: To evacuate NGO workers

Rewards: The Commander Agent gives more

troops to Rescue Entity.

Rewards: The Commander Agent gives land-

based helicopters to Rescue Entity.

Punishments: (obligation) Rescue Entity is obli-

gated to return to the Commander Agent part of their

troops.

Norm 2

Addressee: Rescue Entity

Activation: The weather is bad

Expiration: The weather is good

DeonticConcept: Prohibition

State: To evacuate NGO workers

Punishments: (obligation) Rescue Entity is obli-

gated to return to the Commander Agent part of their

helicopters or land-based helicopters.

Norm 3

Addressee: Rescue Entity

Activation: The weather is bad

Expiration: The weather is good

DeonticConcept: Prohibition

State: To use helicopters.

Rewards: The Commander Agent gives more

troops or land-based helicopters to Rescue Entity.

Punishments: (obligation) Rescue Entity is obli-

gated to return to the Commander Agent part of their

troops.

4 NBDI ARCHITECTURE

The NBDI (Norm-Belief-Desire-Intention) architec-

ture extends the BDI architecture to help agents on

NBDI: AN ARCHITECTURE FOR GOAL-ORIENTED NORMATIVE AGENTS

117

Figure 1: NBDI Architecture.

reasoning about the system norms. Norm is consid-

ered as a primary concept that inﬂuences the agent

while reasoning about its beliefs, desires and inten-

tions. The extensions we have made are represented

in the NBDI architecture by the following compo-

nents, as illustrated in Figure 1: Belief+Norm Review

Function, Norm Selection Function, Norm Filter and

Plans base

(that stores the plans of the agent).

In a nutshell, the NBDI architecture (Figure 1)

works as follows. The agent perceives information

about the world by using its sensors. The sensed

information is the input of the Belief+Norm Review

Function, an extension of Belief Review Function

(Weiss, 1999) deﬁned in the BDI architecture that

is responsible for reviewing the Beliefs base taking

into account the current perception and ones already

stored in the base.

In this work, we consider that norms are also

stored in the Beliefs base, so, besides performing the

original functionality of the Belief Review Function,

the Belief+Norm Review Function is also responsible

for: (i) in case the current perception is a norm, re-

viewing the sets of adopted norms by comparing the

information loaded in the new norm with the norms

and beliefs already stored in the base; and (ii) updat-

ing the sets of adopted and activated norms, consider-

ing that some may become active and others inactive

due to the incoming perceptions.

Next, the Option Generation Function updates the

agent’s desires, and also their priorities. Such adapta-

tion must consider both agent’s current beliefs and in-

tentions, and must be opportunistic, i.e., it should rec-

ognize when environmental circumstances change ad-

vantageously to offer the agent new ways of achieving

intentions, or the possibility of achieving intentions

Plans are composed by actions and states that the agent

has the desire to achieve

that were otherwise unachievable (Weiss, 1999). Note

that this function works exactly as the original func-

tion described in the BDI architecture. This function

does not consider the norms stored in the Beliefs base

while updating the agent desires because the agent

must be able to generate new desires or adapt the ex-

isting ones without the inﬂuence of the norms. Our

architecture considers that the agent is an autonomous

goal-oriented entity that fulﬁls the system norms if it

decides to do so.

After reviewing the beliefs, desires, activated and

adopted norms, the Norm Selection Function is exe-

cuted in order to (i) evaluate the activated norms in

order to select the ones that the agent has the inten-

tion to fulﬁl; and (ii) identify and solve the conﬂicts

among these norms.

Next, the Norm Filter, an extension of Filter

(Weiss, 1999) deﬁned in the BDI architecture, selects

the desires that will become intentions taking into ac-

count the norms the agent wants to fulﬁl. The plans

that will achieve the intentions are also selected by

following the norms the agent wants to fulﬁl.

Finally, the Action Selection Function is respon-

sible for performing the actions speciﬁed by the in-

tention. The next subsections detail the components

added to the original BDI architecture, the one that

was extended and a set of algorithms that demonstrate

how such components can be implemented. Note that

other algorithms to implement such components can

also be developed and plugged into the NBDI archi-

tecture.

4.1 Belief+Norm Review Function

Besides performing the original functionality of the

Belief Review Function, which is the revision of the

beliefs (represented by Reviewing Beliefs task), the

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118

Figure 2: Belief+Norm Review Function.

Belief+Norm Review Function (Figure 2), helps the

agent on recognizing its responsibilities towards other

agents by adopting new norms that specify such re-

sponsibilities (represented by Adopting Norms task)

and updates the sets of activated and adopted norms

(represented by Updating Norms task).

4.1.1 Adopting Norms (AN)

This task recognizes from the set of receiving per-

ceptions the ones that describe norms. After recog-

nizing the norms, such function reviews the set of

adopted norms applying the following veriﬁcations:

(i) it checks if the new norm uniﬁes with one of the

norms already adopted, i.e., if the incoming norm al-

ready exists in the agent Belief Base (Algorithm 1

from line 2 to 7), and (ii) it veriﬁes if the agent is

the addressee of the norm, i.e., if the ﬁeld Addressee

of the new norm uniﬁes with the agent role or agent

name, also stored as a belief in the Belief Base (Algo-

rithm 1 line 8). Finally, such function updates the set

of adopted norms in the Belief Base if the new norm

does not already exist and the agent is the addressee

of the norm (Algorithm 1 line 9).

Algorithm 1 : Adopting Norms.

Require: Beliefs base N: norms stored in the beliefs base

Require: agent: informations about the agent, such as:

name and role

Require: Beliefs base NA: adopted norms stored in the be-

liefs base

Require: NN: new norms

1: for all newNorm in NN do

2: x = true

3: for all n in N do

4: if (n == newNorm) then

5: x = false

6: end if

7: end for

8: if x ∩ ((agent.Name == newNorm.Addresse) ∪

((agent.Role == newNorm.Addresse)) then

9: NA.add(newNorm)

10: end if

11: end for

With the aim to exemplify the use of this task, let’s

consider the scenario presented in Section 3 where

two groups of agents are leaded by Agent A and Agent

B playing the role Rescue Entity. When these enti-

ties receive information about the three system norms,

the AN task is executed checking if the norms are not

stored yet in the agent’s belief base and comparing

the addressee information with the role being played

by the agents.

4.1.2 Updating Norms (UN)

UN task updates the set of activated and adopted

norms checking if the ﬁelds Activation and Expira-

tion of the norm uniﬁes with the beliefs of the agent.

If the activation conditions unify with the beliefs, the

adopted norm is activated (Algorithm 2 from line 3 to

6). If the expiration conditions unify with the beliefs,

the norm is deactivated and stored as an adopted norm

(Algorithm 2 from line 7 to 10).

Algorithm 2 : Updating Norms.

Require: Beliefs base NAD: adopted norms stored in the

beliefs base

Require: Beliefs base NAC: activated norms stored in the

beliefs base

Require: P: new perceptions

1: for all n in (NAD ∪ NAC) do

2: for all p in P do

3: if (n.Activation.unify(p)) then

4: NAD.remove(n)

5: NAC.add(n)

6: else

7: if (n.Expiration.unify(p)) then

8: NAC.remove(n)

9: NAD.add(n)

10: end if

11: end if

12: end for

13: end for

Following the example above, if the weather of

the area operated by one of the two rescue entities

is bad, both norms 2 and 3 are activated, since the

activation condition of both norms is “The weather

is bad”. If the norms are activated, the rescue entity

must not rescue NGO members and must not use he-

licopters. Both norms are deactivated when the expi-

ration condition uniﬁes with the information about a

good weather stored in the agent’s belief base.

4.2 Norm Selection Function

The main goal of the Norm Selection Function (Fig-

ure 3) is to select the norms that the agent has the

intention to fulﬁl. In order to do this, such function

NBDI: AN ARCHITECTURE FOR GOAL-ORIENTED NORMATIVE AGENTS

119

Figure 3: Norm Selection Function.

performs two tasks: 1) Evaluating Norms and 2) Iden-

tifying and Solving Conﬂicts. The ﬁrst task helps the

agent on selecting, from the set of activated norms,

the norms that it has the intention to fulﬁl and the ones

it has the intention to violate. The function evaluates

the beneﬁts of fulﬁlling or violating the norms, i.e., it

checks how close the agent gets of achieving its de-

sires if it decides to fulﬁl or if it decides to violate

the norms. The function groups the activated norms

in two sub-sets: norms to be fulﬁlled and norms to

be violated. Finally, the second task of this function

identiﬁes and solves the conﬂicts among the norms

that the agent has the intention to fulﬁl and among the

ones that the agent has the intention to violate.

4.2.1 Evaluating the Norms (EN)

In order to evaluate the beneﬁts of the fulﬁlment or

violation of a norm according to the agent’s desires

and intentions, the steps below should be followed:

Step-1. In case of obligations, it checks if the state

described in the norm is equal to one of

the states that the agent has desire (or in-

tention) to achieve. In afﬁrmative cases,

the contribution is positive and the function

g(n.DeonticConcept, n.State) returns a value

indicating the level of norm’s contribution

that is calculated according to the priority of

the desire that is similar to the state described

by norm. The function receives as parame-

ters n.DeonticConcept representing the deon-

tic concept type, i. e., obligation or prohibi-

tion, and n.State representing the state that is

been regulated. In any other case, the con-

tribution is zero since it does not disturb the

achievement of the agent’s desires or inten-

tions. Such step is represented in Algorithm

3 from line 2 to 8.

Step-2. In case of prohibitions, it checks if the state

described in the norm is equal to one of the

states that the agent has desire ( or inten-

tion) to achieve. In afﬁrmative cases, the

contribution is negative since it disturbs the

achievement of the agent’s desires or inten-

tions and the function g(n.DeonticConcept,

n.State) calculates the absolute value of the

contribution. In any other case, the prohibi-

tion will contribute neutrally. Such step is

represented in Algorithm 3 between lines 9

and 15.

Step-3. After analyzing the state being regulated, this

step considers the inﬂuence that the rewards

have to the achievement of the agent’s de-

sires. We consider that rewards can never in-

ﬂuence the agent negatively but always pos-

itively or neutrally since they give permis-

sions to achieve a set of states. Such step is

represented in Algorithm 3 by line 16. Func-

tion r(n.Rewards) veriﬁes the desires (or in-

tentions) that are equal to the rewards and re-

turns a value indicating the contribution that

is the sum of the priorities of the agent’s de-

sires beneﬁted by the rewards.

Step-4. Finally, the punishments are evaluated in

order to check if they will inﬂuence the

achievement of the agent’s desires and inten-

tions negatively or positively.

Step-4.1. In case the punishment states a

prohibition and the state being pro-

hibited is one of the agent desires

or intentions, the punishment will

inﬂuence negatively since it will

disturb the agent of achieving one

of its desires. If it is not the case,

the punishment will not inﬂuence.

Such step is represented in Algo-

rithm 4 from line 2 to 8. Function

g(n.punishments.DeonticConcept)

returns a absolute value indicating

the contribution of the fulﬁlment

of the prohibition to the achieve-

ment of the agent’s desires and

intentions.

Step-4.2. In case the punishment states an

obligation and the state being

obliged is one of the agent desires

or intentions, the punishment will

inﬂuence positively (or neutrally)

since such state will already be

achieved by the agent. If it is

not the case, the punishment

will not inﬂuence. Such step

is represented in Algorithm 4

from line 9 to 15. Function

g(n.punishments.DeonticConcept)

returns a value indicating the con-

tribution of the fulﬁlment of the

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120

obligation to the achievement of

the agent’s desires and intentions.

Algorithm 3 : Evaluating the fulﬁlment.

Require: Desires base D

Require: Intentions base I

Require: Beliefs base N: norms stored in the beliefs base

1: x = 0

2: if n.DeonticConcept == Obligation then

3: for all d in (D ∪ I) do

4: if n.State == d then

5: x = x + g (n.DeonticConcept, n.State)

6: end if

7: end for

8: end if

9: if n.DeonticConcept == Prohibition then

10: for all d in (D ∪ I) do

11: if n.State == d then

12: x = x - g (n.DeonticConcept, n.State)

13: end if

14: end for

15: end if

16: x = x + r (n.Rewards)

17: return x

Algorithm 4 : Evaluating the violation.

Require: Desires base D

Require: Intentions base I

Require: Beliefs base N: norms stored in the beliefs base

1: x = 0

2: if n.punishment == Prohibition then

3: for all d in (D ∪ I) do

4: if n.punishment.state == d then

5: x = x - g (n.punishments.DeonticConcept,

n.punishments.State)

6: end if

7: end for

8: end if

9: if n.punishment == Obligation then

10: for all d in (D ∪ I) do

11: if n.punishment.state == d then

12: x = x + g (n.punishments.DeonticConcept,

n.punishments.State)

13: end if

14: end for

15: end if

16: return x

Note that it is necessary to individually check the

contribution of the fulﬁlment and violation of each

norm to the achievement of the agent desires (or in-

tentions). Such step is represented in Algorithm 5 in

the lines 2 and 3.

After checking how each norm can contribute to

the achievement of the agent’s desires and intentions,

the function helps the agent on deciding which are

the norms that it should fulﬁl, i.e., the norms whose

contribution coming from its fulﬁlment is greater than

Algorithm 5 : Reasoning about norms (Main).

Require: fulﬁlSet NF: norms stored in the fulﬁl set

Require: vilateSet NV: norms stored in the violate set

Require: Norms base N

1: for all Norm n in N do

2: fulﬁl = Execute Algorithm 3 using n

3: violate = Execute Algorithm 4 using n

4: if fulﬁl >= violate then

5: NF.add(n)

6: else

7: NV.add(n)

8: end if

9: end for

Algorithm 6 : Detecting Conﬂicts.

Require: fulﬁlSet NF: norms stored in the fulﬁl set

Require: vilateSet NV: norms stored in the violate set

1: for all Norm n1 in NF do

2: for all Norm n2 in NF do

3: if n1.State == n2.State and (n1.DeonticConcept

== Obligation and n2.DeonticConcept == Prohi-

bition) or (n2.DeonticConcept == Obligation and

n1.DeonticConcept == Prohibition) then

4: Execute Algorithm 7 using n1 and n2

5: end if

6: end for

7: end for

8: for all Norm n1 in NV do

9: for all Norm n2 in NV do

10: if n1.State == n2.State and (n1.DeonticConcept

== Obligation and n2.DeonticConcept == Prohi-

bition) or (n2.DeonticConcept == Obligation and

n1.DeonticConcept == Prohibition) then

11: Execute Algorithm 7 using n1 and n2

12: end if

13: end for

14: end for

Algorithm 7 : Solving Conﬂicts.

Require: fulﬁlSet NF: norms stored in the fulﬁl set

Require: vilateSet NV: norms stored in the violate set

1: fulﬁln1 = Execute Algorithm 3 using n1

2: violaten2 = Execute Algorithm 4 using n2

3: fulﬁln2 = Execute Algorithm 3 using n2

4: violaten1 = Execute Algorithm 4 using n1

5: if fulﬁln1 + violaten2 >= fulﬁln2 + violaten1 then

6: NF.remove(n2)

7: NV.remove(n1)

8: else

9: NF.remove(n1)

10: NV.remove(n2)

11: end if

the contribution coming from its violation. Steps 1, 2

and 3 return the contribution of the norm if the agent

chooses to fulﬁl it (Algorithm 3 line 17) and 4.1 and

4.2 return its contribution if the agent chooses to vio-

late the norm (Algorithm 4 line 16). Therefore, Al-

NBDI: AN ARCHITECTURE FOR GOAL-ORIENTED NORMATIVE AGENTS

121

gorithm 3 should be used to evaluate the contribu-

tion of the fulﬁlment of the norm to the agents’ de-

sires/intentions and Algorithm 4 should be used to cal-

culate the contribution of the violation of the norm to

the agents’ desires/intentions.

If the contribution for fulﬁlling the norm is greater

than or equal to the contribution for violating the

norm, the norm is selected to be fulﬁlled and added

to the sub-set Fulﬁll of the activated norms. Such step

is represented in Algorithm 5 from line 4 to 6. Oth-

erwise, it is selected to be violated and added to the

sub-set Violate of the activated norms. Such step is

represented in Algorithm 5 from line 6 to 8.

In order to exemplify the applicability of the EN

task, let’s consider the rescue operation scenario. The

evaluation of the beneﬁts of fulﬁlling and violating

the three norms are shown in Tables 1, 3 and 2 that

indicates the contribution of each norm element to the

achievement of the agent goals. We consider that any

norm element generates the same contribution that is

After analysing the contribution of the norms

shown in Tables 1, 3 and 2, Norm 1 is included in the

set of norms to be fulﬁlled since the contribution for

fulﬁlling it is equal to “+3” and greater than the con-

tribution for violating it that is equal to “-1”. Norm

2 is also included in the fulﬁl set since the contribu-

tion for fulﬁlling it is equal to “-1” and greater than

the contribution for violating it that is equal to “-2”.

And, ﬁnally, Norm 3 is included in the fulﬁl set since

the contribution for fulﬁlling it is equal to “+1” and

greater than the contribution for violating it that is

equal to “-1”. It indicates that the agent has the in-

tention to fulﬁl the three norms.

4.2.2 Detecting and Solving Conﬂicts (DSC)

If two different norms (one being an obligation and

the other one a prohibition) specify the same state, it

is important to check their status, i.e., to check if they

are in the set of norms that will be violated or fulﬁlled

since they may be in conﬂict. As illustrated in Table

4, if the agent intends to fulﬁl the obligation but does

not intend to fulﬁl the prohibition, these norms are not

in conﬂict. The same can be said if the agent intends

to fulﬁl the prohibition and to violate the obligation.

On the other hand, if the agent intends to fulﬁl both

norms or to violate both norms, they are in conﬂict

and it must be solved. Such step is represented in

Algorithm 6.

For instance, in case of conﬂicts between two

norms that the agent intends to fulﬁl or violate, the

one with highest contribution to the achievement of

the agent’s desires (and intentions) can be selected.

If the contributions have equal values we can choose

Table 1: Evaluating norm 1.

Norm Contribution

positive neutral negative

Obligation 1 0 0

Reward 1 0 0

Punishments

Obligation 0 0 1

Table 2: Evaluating norm 3.

Norm Contribution

positive neutral negative

Prohibition 0 0 1

Reward 1 0 0

Punishments

Obligation 0 0 1

Table 3: Evaluating norm 2.

Norm Contribution

positive neutral negative

Prohibition 0 0 1

Punishments

Obligation 0 0 1

Table 4: Detecting Conﬂicts. P represents the prohibition

and O the obligation that describe the same state.

Fulﬁl (O) Violate (O)

Fulﬁl (P) Conﬂict Fulﬁl (P)

Violate (P) Fulﬁl (O) Conﬂict

anyone. That is, if the contribution coming from the

fulﬁlment of the ﬁrst norm (Steps 1, 2 and 3) plus the

contribution coming from the violation of the second

norm (Steps 4.1 and 4.2) is greater to or equal than the

contribution coming from the fulﬁlment of the second

norm plus the contribution coming from the violation

of the ﬁrst norm, the ﬁrst norm is selected to be ful-

ﬁlled and the second one to be violated. It is repre-

sented in Algorithm 7 from line 5 to 8. In case the

opposite happens, the second norm is selected to be

fulﬁlled and the ﬁrst to be violated, as described in

Algorithm 7 from line 8 to 11.

Considering the norms evaluated in the EN func-

tion, a conﬂict between Norm 1 and 2 is detected and

should be solved. The conﬂict is solved by selecting

Norm 1 to be fulﬁlled and Norm 2 to be violated since

the contribution coming from the fulﬁlment of the ﬁrst

norm (+3) plus the contribution coming from the vi-

olation of the second norm (-2) is greater than the

contribution coming from the fulﬁlment of the sec-

ond norm (-1) plus the contribution coming from the

violation of the ﬁrst norm (-1).

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122

Figure 4: Norm Filter.

4.3 Norm Filter

The Norm Filter (Figure 4) is executed in order to

drop any intention that does not bring beneﬁts to the

agent, retains intentions that are still expected to have

a positive overall beneﬁt and adopt new intentions, ei-

ther to achieve existing intentions, or exploit new op-

portunities. In order to accomplish theses tasks and

besides performing the original functionality of the

Filter function, such function performs two additional

steps.

4.3.1 Selecting Desires (SD)

In this step the ﬁlter selects the desires that will be-

come intentions taking into account the norms the

agent wants to fulﬁl. The desires are selected accord-

ing to their priorities and the norms may increase or

decrease such priorities. If the agent has a desire to

achieve a state and there is a norm that obliges the

agent of achieving such state, the desire priority is in-

creased according to the importance of the norm (rep-

resented in Algorithm 8 from line 3 to 5). If the agent

has a desire to achieve a state and there is a norm that

prohibits the agent of achieving such state, the desire

priority is decreased according to the importance of

the norm (represented in Algorithm 8 from line 5 to

9). If there is not any norm related to the desires, its

priority is not modiﬁed. Finally, the function getDe-

sireHighestPriority() (Algorithm 8 line 12) returns the

desire with highest priority.

By applying this function to our example, the goal

“Evacuating the members of a NGO to a safe loca-

tion” is selected because such goal has highest prior-

ity since it receives a positive inﬂuence of Norm 1.

4.3.2 Selecting Plans (SP)

After selecting the desires with highest priorities, i.e.,

after generating the agent intentions, the agent needs

to select the plans that will achieve such intentions.

Algorithm 8 : Selecting Desires.

Require: fulﬁlSet NF: norms stored in the fulﬁl set

Require: Desires base D

Require: Intentions base I

1: for all Norm n in NF do

2: for all d in (D ∪ I) do

3: if (n.State == d) ∩ (n.DeonticConcept ==

Obligation) then

4: d.annotatePriority(+1)

5: else

6: if (n.State == d) ∩ (n.DeonticConcept ==

Prohibition) then

7: d.annotatePriority(-1)

8: end if

9: end if

10: end for

11: end for

12: return getDesireHighestPriority()

Like in the selection of desires, the selection of plans

will also be inﬂuenced by the norms.

While selecting a plan it is important to make sure

that such plan will achieve the state described in the

obligation norm and that will not achieve an state be-

ing prohibited. Therefore, the plans that achieve a

given intention are ordered according to their priori-

ties. If the state described by an obligation norm is

equal to one of the states included in the plan, the

norm increases the priority of such plan (represented

in Algorithm 9 from line 4 to 6). Otherwise, if the

state described by an prohibition norm is equal to one

of the states included in the plan, the norm decreases

the priority of such plan (represented in Algorithm 9

from line 6 to 10).

Let’s consider that the desires of the agents in

our example with highest priority is “Evacuating the

members of a NGO to a safe location”, that the agent

has the intention to fulﬁl Norm 3, and that the priority

of plans that uses helicopters has decrease. When SP

step is executed it selects the plan with highest prior-

ity that tries to rescue the NGO workers and that will

not use helicopters to do so.

5 IMPLEMENTING THE NBDI

ARCHITECTURE

The NBDI architecture proposed in section 4 was im-

plemented by translating the functions related to re-

view, select and ﬁlter norms proposed in such archi-

tecture to the Normative Jason platform, described in

(Neto et al., 2010). The platform provides a set of

normative functions, as follows: (i) the Norm Re-

view function helps the agent on recognizing its re-

NBDI: AN ARCHITECTURE FOR GOAL-ORIENTED NORMATIVE AGENTS

123

Algorithm 9 : Selecting Plan.

Require: fulﬁlSet NF: norms stored in the fulﬁl set

Require: P: all plans that achieve the selected desire

1: for all Norm n in NF do

2: for all p in P do

3: for all state in p do

4: if (n.State == state)∩ (n.DeonticConcept ==

Obligation) then

5: p.annotatePriority(+1)

6: else

7: if (n.State == state) ∩

(n.DeonticConcept == Prohibition)

then

8: p.annotatePriority(-1)

9: end if

10: end if

11: end for

12: end for

13: end for

14: return getPlanHighestPriority()

sponsibilities towards other agents by incorporating

the norms that specify such responsibilities. Such

function implements the Algorithm 1 described in the

(AN) step; (ii) the main task of the Updating Norm

function is to update the set of activated and adopted

norms. It implements the algorithm Algorithm 2 de-

scribed in the (UN) step; (iii) the Evaluating Norm

function helps the agent on selecting the norms that it

has the intention to fulﬁl and the ones it has the inten-

tion to violate by executing the Algorithm 5 and, con-

sequently, Algorithms 3 and 4 described in the (EN)

step; (iv) the Detecting and Solving Conﬂicts func-

tion checks and solves the conﬂicts among the norms.

It executes Algorithms 6 and 7 described in the (ISC)

step; (v) the Selecting Desires function selects the de-

sires that will become intentions taking into account

their priorities and executes Algorithm 8 described in

the (SD) step; and, ﬁnally, (vi) the Selecting Plans

function chooses a single applicable plan from the set

of options based on their priorities and executes Algo-

rithm 9 described in the (SP) step. The applicability

of the NBDI architecture and its implementation was

demonstrated by the developing of the non-combatant

evacuation scenario, presented in section 3.

6 RELATED WORK

Our work was inﬂuenced by the architecture proposed

in (Castelfranchi et al., 1999). Such architecture to

build normative agents also contemplates functions to

deal with the adoption of norms and the inﬂuence of

norms on the selection of desires and plans. However,

our work presents details about the veriﬁcations that

must be satisﬁed in order to agents adopt norms, the

evaluations that must be made to select the norms the

agents intend to fulﬁl and violate and a guidelines to

help agents on selecting plans according to the norms

they want to fulﬁl and violate.

The BOID (Belief-Obligation-Intention-Desire)

architecture proposed in (Broersen et al., 2002) and

extended in (Beavers and Hexmoor, 2002) is an exten-

sion of the BDI architecture that considers the inﬂu-

ence of beliefs, obligations, intentions and desires on

the generation of the agent desires. The BOID archi-

tecture applies the notion of agent types to help on the

generation of the desires. For instance, if the agents

beliefs override its obligations, intentions or desires,

then we say that this agent is realistic, if its desires

override its obligations, then the agent is called self-

ish and if its obligations override its desires, then the

agent is called social. Thus, their approach could have

been used in the (SD) function being proposed in our

paper since this function is the one responsible to se-

lect the desires. Instead of basing the selection of de-

sires on the agent type, we have used the norm contri-

bution and the priority of the desires (and intentions)

to provide a quantiﬁable solution to the selection of

the agent desire.

The approach described in (Kollingbaum, 2005)

proposes an architecture to build norm-driven agents

whose main purpose is the fulﬁlment of norms and not

the achievement of their goals. In contrast, our agents

are desire-driven that take into account the norms but

are not driven by them.

In (Meneguzzi and Luck, 2009) the authors pro-

vide a technique to extend BDI agent languages by

enabling them to enact behaviour modiﬁcation at run-

time in response to newly accepted norms, i.e., it con-

sists of creating new plans to comply with obliga-

tions and suppressing the execution of existing plans

that violate prohibitions. However, they have not

considered the desires and plans priorities. In our

work we consider that obligations and prohibitions

may increase or decrease the priority of a desire or

a plan, and that the selection of desires and plans

are based on their priorities. The approach proposed

in (Meneguzzi and Luck, 2009) simply creates, se-

lects or suppresses plans in response to obligations

and prohibitions. As a consequence, it is possible that

desires and plans with high priorities are not selected.

The agents built according to the architecture pre-

sented in (Lopez-Lopez and Marquez, 2004) are able

to evaluate the effects of norms on their desires help-

ing then on deciding to comply or not with the norms.

This architecture is based on the BDI architecture

whose properties have been expanded to include nor-

mative reasoning. In the Norm Review process being

ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence

124

proposed, we extend some of the veriﬁcations deﬁned

in (Lopez-Lopez and Marquez, 2004), such as: our

architecture checks (i) if the norm was not adopted

already and (ii) if the agent is the addressee of the

norm. Besides, in the Norm selection process, al-

though the approach proposed in (Lopez-Lopez and

Marquez, 2004) evaluates the positive and negative

effects of norms on the agent desires, it does not con-

sider the inﬂuence of rewards in such evaluation.

The authors in (Dignum, 1996) present concepts,

and their relations, that are used for modelling au-

tonomous agents in an environment that is governed

by some (social) norms. Although such approach

considers that the selection of desires and plans

should be based on their priorities and that such pri-

orities can be inﬂuenced by norms, it does not present

a complete strategy with a set of veriﬁcation in the

norm review process, and strategies to evaluate, iden-

tify and solve conﬂicts between norms such as our

work does.

7 CONCLUSIONS

This paper proposes an extension to the BDI architec-

ture called NBDI to build goal-oriented agents able

to: (i) check if the agent should adopt or not a norm,

(ii) evaluate the pros and cons associated with the ful-

ﬁlment or violation of the norm, (iii) check and solve

conﬂicts among norms, and (iv) choose desires and

plans according to their decisions of fulﬁlling or not a

norm.

By implementing the algorithms from 1 to 9 and

using the Normative Jason platform, the applicability

of NBDI architecture could be veriﬁed in the example

presented in Section 3. Such agents are responsible

to plan the evacuation of people that are in hazardous

location, check the incoming perceptions (including

norms), select the norms they intend to fulﬁl based

on the beneﬁts they provide to the achievement of the

agent’s desires and intentions, identify and solve con-

ﬂicts among the selected norms, and decide to cope or

not with the norms while dropping, retaining or adopt-

ing new intentions. We are investigating the need for

extenting the AgentSpeak language with new predi-

cates that better represent the norms. We are also in

the process of deﬁning an experimental study in order

compare our approach with other related ones.

ACKNOWLEDGEMENTS

The present work has been partially funded by the

Spanish project Agreement Technologies” (CON-

SOLIDER CSD2007-0022, INGENIO 2010) and by

the Brazilian research councils CNPq under grant

303531/2009-6, 557.128/2009-9 and FAPERJ under

grant E-26/110.959/2009, E- 26/170028/2008.

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