Indirect Normative Conflict
Conflict that Depends on the Application Domain
Viviane Torres da Silva
1
, Christiano Braga
2
and Jean de Oliveira Zahn
2
1
IBM Research (on leave from Universidade Federal Fluminense), Rio de Janeiro, Brazil
2
Computer Science Department, Universidade Federal Fluminense, Niterói, Brazil
Keywords:
Agents, Norms, Conflicts, Application Domain.
Abstract:
Norms are being used as a mechanism to regulate the behavior of autonomous, heterogeneous and indepen-
dently designed agents. Norms describe what can be performed, what must be performed, and what cannot be
performed in the multi-agent systems. Due to the number of norms specified to govern a multi-agent system,
one important issue that has been considered by several approaches is the checking for normative conflicts.
Two norms are said to be in conflict when the fulfillment of one norm violates the other and vice-versa. In
this paper, we formally define the concept of an indirect normative conflict as a conflict between two norms
that not necessarily have contradictory or contrary deontic modalities and that may govern (different but) re-
lated behaviors of (different but) related entities on (different but) related contexts. Finally, we present an
ontology-based indirect norm conflict checker that automatically identifies direct and indirect norm conflicts
on an ontology describing a set of norms and a set of relationships between the elements identified in the
norms (behavior, entity and context).
1 INTRODUCTION
Norms have been significantly used as a mechanism
to regulate the behavior of autonomous, heteroge-
neous and independently design agents. Therefore,
several approaches have focused on the identification
and resolution of conflicts between pairs of norms.
The majority (Kollingbaum et al., 2008b; Vasconce-
los and Norman, 2009) check for direct and simple
normative conflict between two norms that have con-
tradictory deontic modalities, such as prohibition and
permission, or contrary deontic modalities, such as
prohibition and obligation, (Elhag et al., 1999), and
that are defined in the same context governing the
same behavior executed by the same entity. An exam-
ple of such direct conflict is when one norm prohibits
and another allows an agent to execute a given action
in a given context.
Few approaches focus on the identification of in-
direct conflicts between pairs of norms. A normative
conflict is called indirect when the norms in conflict
regulate the behavior of (different but) related enti-
ties; govern (different but) related behaviors, or are
defined in (different but) related contexts. In addition,
an indirect conflict can occur even thought the norms
in conflict do not have contradictory or contrary de-
ontic modalities. In (Oren et al., 2008) the authors
consider that two obligation norms are in conflict if
the entity (whose behavior is being governed by the
norms) is not able to execute both obligations at the
same time. In (Kollingbaum et al., 2008a; Vasconce-
los et al., 2007; Vasconcelos et al., 2009) two norms
are considered to be in conflict not only when they
govern the same behavior but also when they govern
related ones. However, to the best of our knowledge,
the following situations are not considered simultane-
ously:
1. Conflicts between norms that do not have contra-
dictory or contrary deontic modalities;
2. Conflicts between norms that regulate the behav-
ior of (different but) related entities; govern (dif-
ferent but) related behaviors, and are defined in
(different but) related contexts.
In this paper we consider two norms to be in in-
direct conflicts if they fall within the situations de-
scribed in 1 and 2. As an example of such a situation,
let us suppose that there is a norm obliging attendees
to make silence during talks and another allowing stu-
dents to ask questions during classes. In the tradi-
tional or direct conflict identification approach, a con-
flict between these two norms would not be detected
452
Torres da Silva V., Braga C. and de Oliveira Zahn J..
Indirect Normative Conflict - Conflict that Depends on the Application Domain.
DOI: 10.5220/0005350304520461
In Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS-2015), pages 452-461
ISBN: 978-989-758-096-3
Copyright
c
 2015 SCITEPRESS (Science and Technology Publications, Lda.)

since they do not mention contradictory or contrary
deontic modalities and the norms are not applied to
the same entities, behaviors and contexts. However,
we can say that there is an indirect conflict between
these norms if we consider relationships among el-
ements identified in the norms. For instance, let us
consider the following scenario: (i) a student is an at-
tendee; (ii) a class is a talk; and (iii) to make silence
is orthogonal to ask questions, i.e., actions “to make
silence” and “to ask questions” cannot be executed
by the same or related entities at the same time. The
conflict between the norms is not explicit (or direct)
but camouflaged by the relationships between the ele-
ments addressed by the norms. We call such a conflict
indirect.
We also introduce OnCheckIn, an ontology-based
normative conflict checker that identifies both direct
and indirect conflicts between two norms. It receives
as input an ontology describing the norms and the re-
lationships on entities, behaviors and contexts. Con-
sidering relationships between contexts and entities, it
applies a set of norm propagation rules that propagate
regulation from a context (or entity) to other contexts
(or entities) related to it. Next, the conflict checker
considers the relationship between the behaviors be-
ing regulated by the propagatednorms to identify con-
flicts. When norms are propagated, indirect conflicts
are made explicit and easily identified through the
analysis of the relationships between behaviors. Thus,
OnCheckIn is able to detect indirect conflicts between
norms through norm propagation by taking into ac-
count the relationships among norm elements, that is,
contexts, entities and behavior.
Is worth noting that we do not aim at an exhaus-
tive or complete set of relationships between contexts,
entities and behaviors. We propose an extensible ap-
proach where other relationships can be defined to-
gether with their propagation and conflict rules.
The remainder of this paper is organized as fol-
lows. Section 2 formalizes the concepts of norm
and regulation that we will use throughout this paper.
Sections 3 and 4 formalize the relationships between
contexts and entities and define the semantics of the
norm propagation restrictions associated with these
relationships. Section 5 discusses the correctness of
the norm propagation approach by proving that norm
propagation does not add or remove regulation. Sec-
tion 6 describes the relationships between behaviors
and the conflict rules defined to identify conflicts.
Section 7 presents OnCheckIn, our ontology-based
tool for direct and indirect normative conflict detec-
tion. Section 8 describes related work. Section 9 con-
cludes this paper with final remarks and future work.
2 NORM AND REGULATION
DEFINITION
Our norm definition essentially
1
follows (Figueiredo
and Silva, 2011) where, after studying ten specifica-
tions and implementations of normative languages,
the authors have identified common elements to de-
scribe a norm.
Definition 1 (Norm). A norm is a tuple in the set
Norm
Norm = D×C× {E ∪ {_}} × B× Cnd× Cnd
where D denotes the deontic modalities (obligation
(ob), prohibition (pr) and permission(pe)); C is the
context where the norm is defined; E is the entity
whose behavior is being regulated by the norm; B is
the behavior being regulated (i.e., an action); and C
n
indicates the conditions that activate and deactivate
the norm, respectively. The symbol “_” denotes that
a given norm regulates all entities in a given context.
The scope of a norm is defined as its context.
The entity, whose behavior is being regulated,
must fullfil the norm when executing in the context
where the norm is being defined. Outside its context
the norm is not valid. In this paper, we consider that a
norm can be defined in the context of an organization
o ∈ O or of an environment env ∈ Env. Thus, the set
of possible contexts are defined as C = O ∪ Env. A
norm regulates the behavior of an agent a ∈ A, an or-
ganization (or group of agents) o ∈ O or a role r ∈ R.
Agents, organizations and roles are elements of the
set E = A ∪ R∪ O.
The activation condition acc ∈ Cnd and deactiva-
tion condition dac ∈ Cnd state the occurrence of an
event such as a date, the execution of an action, or
the fulfillment of a norm. In this paper, we will con-
sider events to be natural numbers since we are mainly
interested in the order of events occurrences rather
than its structure. Thus, we use simple mathematic
symbols such as ≤ and ≥ to indicate that an event
occurs before or after another in a formula such as
(∀n ∈ Norm)(n.acc ≤ n.dac) meaning that it should
be true that, for all norms, the activation condition of
a given norm n should happen before the deactivation
condition of it. We use the “dot notation”, as in n.acc,
to denote the appropriate projection of a given tuple,
such as the activation condition of a norm.
1
The main differences between our norm definition and
the one presented in (Figueiredo and Silva, 2011) are: (i)
we have suppressed the definition of sanctions and rewards
since they do not influence on the checking of conflicts and
(ii) we are considering that a norm cannot only restrict the
execution of an action but also the achievement of a state,
as will be discussed in Section 5.
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A norm can implicitly identify the agents and
organizations whose behaviors are being regulated.
Some norms use the symbol “_” to indicate that the
they are applied to all agents and organizations exe-
cuting in the contexts identified in the norms. Other
norms may refer to roles played by agents or organi-
zations in order to indicate that they apply to all agents
and organizations that play such roles. In order to be
able to explicitly identify the entities whose behaviors
are being regulated by those norms, we have defined
the regulation function. Regulation is able to iden-
tify all entities whose behavior is being regulated by a
norm based on the relationships between contexts and
entities identified in the domain ontology.
Definition 2 (Regulation). A regulation identifies the
entities whose behavior is being regulated by a given
norm. Its input parameters are one norm and the set
of relationships defined between entities and contexts
that are represented in the domain ontology.
regulation : Norm× Rel → E
3 CONTEXT RELATIONSHIPS
In this Section, we recall two well-
known (Wooldridge, 2000; Weyns et al., 2005;
W3C, 2004; Hubner et al., 2007) relationships among
contexts: inhabit, in Definition 3, and hierarchy, in
Definition 5. Associated with each relationship, we
define the semantics of norm propagation constraints
by using First-order logic. By norm propagation we
mean that if a norm is solely defined at the level
of a context then there must exist norms specifying
the same regulation at the level of the elements of
the given context. In this Section, we define, for
each context relationship, a propagation constraint
that formalizes regulation propagation for the given
context relationship.
Definition 3 (Inhabit). Relationship inhabit relates an
entity to the environment where it executes. The enti-
ties that execute in the context of an environment are
agents and organizations.
Organization = P (Agent)
inhabit ⊆ Environment× (Agent∪ Organization)
where P is the powerset operation.
The norm defined in the context of an environment
regulates the behavior of the agents and organizations
executing in such a scope. If a norm is defined in the
scope of an environment and it does not identify the
entity which behavior is being regulated, such a norm
applies to all entities that inhabit the environment. In
order to model such norm, it is only necessary to use
symbol “_” instead of explicitly identifying the enti-
ties. Thus, if we apply the regulation function (see
Definition 2) to such a norm, it will return all agents
and organizations that inhabit the environment iden-
tified in the norm, what indicates that such a norm
governs the behavior of such entities.
Definition 4 (Inhabit Propagation). If a norm is de-
fined solely in the context of an environment, that is,
it does not mention a specific entity, then it applies to
all entities that inhabit the given environment,
∃env(hd, env, _, b, acc, daci ∈ Norm →
∀e(inhabit(env, e) →
hd, env, e, b, acc, daci ∈ Norm))
where d ∈ D,env ∈ Env, e ∈ A∪ O, b ∈ B, acc ∈ Cnd,
dac ∈ Cnd.
If there is an environment and a norm applied to
such environment, all entities that inhabit such envi-
ronment will be regulated by a norm with the same
characteristics as the first one.
Definition 5 (Hierarchy). Relationship hierarchy re-
lates an organization (called superorganization) to
another organization (called suborganization) exe-
cuting in the context of the superorganization.
hierarchy ⊆ Organization× Organization
If a norm is defined in the scope of an organization
and it does not make it explicit the entity whose be-
havior is being regulated, then such a norm applies to
all suborganizations of such an organization, as for-
malized by Definition 6, and to all roles of such an
organization, as formalized by Definition 10. Thus,
if we apply the regulation function (see Definition 2)
to such a norm, it will return all suborganizations and
roles played the organization identified as the context
of the norm. It indicates that such a norm governs the
behavior of the suborganizations and roles.
Definition 6 (Hierarchy Propagation). If a norm is
defined in the context of an organization and does not
mention a specific entity then it applies to all subor-
ganizations of the given organization.
∃o
1
(hd, o
1
, _, b, acc, daci ∈ Norm →
∀o
2
(hierarchy(o
1
, o
2
) →
hd, o
1
, o
2
, b, acc, daci ∈ Norm))
where d ∈ D, o
1
∈ O, o
2
∈ O, b ∈ B, acc ∈ Cnd, dac ∈
Cnd.
4 ENTITY RELATIONSHIPS
Entity relationships relate agents and organizations to
the roles they can play and roles to the organizations
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where they are defined. In this section, we formalize
these relationships and the propagation restrictions
related to such relationships.
Definition 7 (Play). Relationship play relates an
agent or suborganization to the roles that it can play
in the context of a given organization.
play ⊆ Organization× (Agent∪Organization)×Role
When a norm is applied to a role, it regulates the
behavior of the agents or organizations that play such
role in the context where the norm was defined. Thus,
if we apply the regulation function (see Definition 2)
to such a norm, it will return all agents and organiza-
tions playing the role in such context, what indicates
that such a norm governs the behavior of the agents
and organizations.
Definition 8 (Play Propagation). If a norm regulates
a role in a given organization then it regulates the
behavior of all agents and suborganizations that can
play the same role in the given organization.
∃r(hd, o, r, b, acc, daci ∈ Norm) →
∀e(play(o, e, r) → hd, o, e, b, acc, daci ∈ Norm))
where d ∈ D, o ∈ O, r ∈ R, b ∈ B, e ∈ A ∪ O, acc ∈
Cnd, and dac ∈ Cnd.
If there is a role and a norm applied to such role
in a given organization, all entities that play such role
in such organization will be regulated by a norm with
the same characteristics as the first one.
Definition 9 (Ownership). Relationship ownership
relates a role to an organization where the role is de-
fined.
ownership ⊆ Organization× Role
If a norm is defined in the scope of an organiza-
tion and does not specific the entity whose behavior
should be regulated, such a norm applies to all roles
defined in the scope of such an organization. Thus, if
we apply the regulation function (see Definition 2) to
such a norm, it will return all roles played in such an
organization, what indicates that such a norm governs
the behavior of all agents and organizations that play
such roles.
Definition 10 (Ownership Propagation). If a norm is
applied to all entities in an organization then it must
be applied to all roles played in the organization.
∃o(hd, o, _, b, acc, daci ∈ Norm →
∀r(ownership(o, r) →
hd, o, r, b, acc, daci ∈ Norm))
where d ∈ D, o ∈ O, r ∈ R, b ∈ B, acc ∈ Cnd, and
dac ∈ Cnd.
5 CORRECTNESS OF NORM
PROPAGATION
The aim of this section is to demonstrate that, given a
set of norms, the norm propagation restrictions speci-
fied in Sections 3 and 4 preserve conflicts in the given
set of norms, i.e., they do not include or remove any
normative conflict. Essentially, the propagation re-
strictions only require a given set of norms to make
explicit regulations that were otherwise implicit as
they are defined at the level of composite elements
such as contexts or entities. In other words, no new
regulation is created or removed by norm propaga-
tion.
Lemma 1 (Inhabit Propagation Correctness). Inhabit
propagation preserves regulation.
Proof. Let N be a set of norms with n ∈ N, n =
hd, env, _, b, acc, daci, that is, n is a norm defined
solely in the context of the environment env. There-
fore, by applying regulation function (see Defini-
tion 2), n governs the behavior of all agents and orga-
nizations that inhabit env. By Definition 4, the propa-
gation of n requires norms n
e
= hd, env, e, b, acc, daci,
where inhabit(env, e), also to be in N. The propaga-
tion of n does not imply any new regulation in N that
was not already specified by n. The entities whose
behaviors were regulated by n are exactly the same
entities whose behaviors are being regulated by the
set of n
e
. In addition, since n
e
is propagated based
on n, its other parameters have not been changed, as
shown in Definition 4.
Lemma 2 (Hierarchy Propagation Correctness). Hi-
erarchy propagation preserves regulation.
Proof. Let N be a set of norms with n ∈ N, n =
hd, org, _, b, acc, daci, that is, n is a norm defined
solely in the context of an organization org. There-
fore, by applying regulation function (see Defini-
tion 2), n governs the behavior of all suborganiza-
tions of org. By Definition 6, the propagation of n re-
quires norms n
s
= hd, org, suborg, b, acc, daci, where
hierarchy(org, suborg), also to be in N. The propa-
gation of n does not imply any new regulation in N
that was not already specified by n. The suborganiza-
tions whose behaviors were regulated by n are exactly
the same suborganizations whose behaviors are being
regulated by the set of n
s
. In addition, since n
s
is prop-
agated based on n, its other parameters have not been
changed, as shown in Definition 6.
Lemma 3 (Play Propagation Correctness). Play
propagation preserves regulation.
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Proof. Let N be a set of norms with n ∈ N, n =
hd, o, r, b, acc, daci, that is, n is a norm defined in
the context of an organization o and applied to role
r. Therefore, by applying regulation function (see
Definition 2), n governs the behavior of all agents
or organizations playing role r in o. By Defini-
tion 8, the propagation of n requires norms n
e
=
hd, o, e, b, acc, daci, where play(o, e, r), also to be in
N. The propagation of n does not imply any new reg-
ulation in N that was not already specified by n. The
agents and organizations whose behaviors were regu-
lated by n are exactly the same agents and organiza-
tions whose behaviors are being regulated by the set
of n
e
. In addition, since n
e
is propagated based on n,
its other parameters have not been changed, as shown
in Definition 8.
Lemma 4 (Ownership Propagation Correctness).
Ownership propagation preserves regulation.
Proof. Let N be a set of norms with n ∈ N, n =
hd, o, _, b, acc, daci, that is, n is a norm defined in
the context of an organization o. Therefore, by ap-
plying regulation function (see Definition 2), n gov-
erns the behavior of all role r played in o. By Def-
inition 10, the propagation of n requires norms n
r
=
hd, o, r, b, acc, daci, where ownership(o, r), also to be
in N. The propagation of n does not imply any new
regulation in N that was not already specified by n.
The roles governed by n are exactly the same roles
governed by the set of n
e
. In addition, since n
e
is
propagated based on n, its other parameters have not
been changed, as shown in Definition 10.
Theorem 1 (Norm propagation correctness). Norm
propagation does not add or remove regulation of a
given set of norms.
Proof. By lemmata 1 to 4.
6 ACTION RELATIONSHIPS
The following subsections define four kinds of re-
lationships used to relate actions. These relation-
ships were selected because they are well-known in
the planning literature and the majority has been for-
malized in TAEMS (Horling et al., 1999)
2
, a mod-
eling language used to describe task structures and
problem-solving activities of intelligent agents in a
multi-agent environment. As stated before, is not our
goal to be exhaustive on identifying all possible rela-
tionships among actions. We propose an extensional
2
Refinement is equivalent to max, composition is equiv-
alent to sum and dependency is equivalent to enables. Max,
sum and enable were defined in TAEMS.
approach where the set of relationships among actions
can be conservatively extended, that is, preserving the
semantics of the basic relationships defined here.
In this paper, we consider a conflict is represented
as a predicate between two norms.
Definition 11 (Conflict). A conflict is a predicate be-
tween two norms.
conflict : Norm× Norm → {true, false}
To check for conflicts that occur due to the rela-
tionships among actions in the norms, we define ac-
tion conflict rules among related actions. The conflict
rules consider that all propagation restrictions (from
Definitions 4, 6, 8 and 10) are true in a given set of
Norm individuals. Conflict rules are thus defined on
pairs of norms where their contexts and the entities
whose behavior is being regulated by the norms are
the same.
6.1 Action Refinement
Action refinement relates two actions: a subaction
and a superaction. The execution of the subaction
must achieve the goal of executing the superaction,
and possibly other goals. If there is more than one
subaction for a given superaction, the execution of
some subaction must achievethe goal of executingthe
superaction. We have defined the conflict rules from
the point of view of the superaction.
In order to give a simple example of action refine-
ment, let’s consider the actions to move, to fly and to
taxi. The last two actions are subactions of the super-
action to move, i.e., when a plane flies or taxis it is
moving.
Definition 12 (Refinement). Relationship refinement
relates an action (called superaction) to another ac-
tion (called subactions).
refinement ⊆ Action× Action
Definition 13 (Refinement Conflict Rule for Prohibi-
tion). If there is a prohibition applied to a superaction
and an obligation or permission applied to a subac-
tion then these norms are in conflict.
∃b
1
∃b
2
((hpr, c, e, b
1
, acc
1
, dac
1
i ∈ Norm ∧
hd, c, e, b
2
, acc
2
, dac
2
i ∈ Norm ∧
refinement(b
1
, b
2
)) →
conflict(hpr, c, e, b
1
, acc
1
, dac
1
i,
hd, c, e, b
2
, acc
2
, dac
2
i))
where d ∈ {ob, pe}, c ∈ C, e ∈ E, b
1
and b
2
∈ B, acc
i
and dac
i
∈ Cnd, acc
1
≥ dac
2
, and dac
1
≥ acc
2
.
If there is two behaviors b
1
and b
2
, a norm pro-
hibiting the execution of b
1
, another norm obligating
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or permitting the execution of b
2
, and the relationship
refinement is defined between b
1
and b
2
, then such
norms are in conflict.
Definition 14 (Refinement Conflict Rule Schema for
Obligation and Permission). If there is an obligation
or permission applied to a superaction and prohibi-
tions applied to all subactions then these norms are
in conflict.
∃b
1
(hd, c, e, b
1
, acc
1
, dac
1
i ∈ Norm →
∀b
i
(refinement(b
1
, b
i
) →
hpr, c, e, b
i
, acc
2
, dac
2
i ∈ Norm →
conflict(hd, c, e, b
1
, acc
1
, dac
1
i,
hpr, c, e, b
i
, acc
2
, dac
2
i))
where b
i
∈ {b | refinement(b
1
, b)}, d ∈ {ob, pe}, c ∈
C, e ∈ E, b
1
and b
2
∈ B, acc
i
and dac
i
∈ Cnd, acc
1
≥
dac
2
, and dac
1
≥ acc
2
.
If there is a behavior b
1
and an obligation or pro-
hibition applied to b
1
, and if all behaviors b
i
that are
refinements of b
1
have a norm prohibiting their exe-
cution, a conflict exists between the norm applied to
b
1
and the one applied to b
i
.
6.2 Action Composition
If a composition relationship is defined between two
actions, then there is an action called part that is part
of an action called whole and the whole action is an
abstract action. In order to achieve the goals of exe-
cuting the whole action it is necessary to execute all
its parts. Since the norms defined to the whole action
influence the execution of the part actions, we have
defined the conflict rules from the point of view of the
whole action.
Let’s consider there is a need to govern a multi-
agent system. Such an action implies the execution
of three other actions: to find out violations, to find
out fulfillments and to apply sanctions. The action to
govern is composed of these three actions.
Definition 15 (Composition). Relationship composi-
tion relates an action (called whole) to other actions
(called parts).
composition ⊆ Action× Action
Definition 16 (Composition Conflict Rule for Obliga-
tion and Permission). If an obligation or permission
is defined for a whole action and a prohibition is de-
fined for a part action then there is a conflict.
∃b
1
∃b
2
((hd, c, e, b
1
, acc
1
, dac
1
i ∈ Norm∧
hpr, c, e, b
2
, acc
2
, dac
2
i ∈ Norm∧
composition(b
1
, b
2
)) →
conflict(hd, c, e, b
1
, acc
1
, dac
1
i,
hpr, c, e, b
2
, acc
2
, dac
2
i))
where d ∈ {ob, pe}, c ∈ C, e ∈ E, b
1
and b
2
∈ B, acc
i
and dac
i
∈ Cnd, acc
2
≥ acc
1
,and dac
1
≥ dac
2
.
Definition 17 (Composition Conflict Rule Schema for
Prohibition). If there is a prohibition applied to a
whole action then a conflict will arise if all part ac-
tions are being obliged or permitted.
∃b
1
(hpr, c, e, b
1
, acc
1
, dac
1
i ∈ Norm →
∀b
i
(composition(b
1
, b
i
) →
conflict(hpr, c, e, b
1
, acc
1
, dac
1
i,
hd, c, e, b
i
, acc
2
, dac
2
i))
where b
i
∈ {b | composition(b
1
, b)}, d ∈ {ob, pe}, c ∈
C, e ∈ E, b
1
and b
2
∈ B, acc
i
and dac
i
∈ Cnd, acc
2
≥
acc
1
,and dac
1
≥ dac
2
.
6.3 Action Orthogonality
If there exists an orthogonal relationship between two
actions then both actions cannot be executed at the
same time by the same entity. For instance, let’s con-
sider the action to fly and to taxi. A plane cannot exe-
cute these two actions at the same time.
Definition 18 (Orthogonality). Relationship orthogo-
nal relates two actions that cannot be executed simul-
taneously.
orthogonal ⊆ Action× Action
Definition 19 (Orthogonality Conflict Rule). Orthog-
onal actions are in conflict if they are both obliga-
tions, or one is an obligation and the other a permis-
sion.
∃b
1
∃b
2
((hob, c, e, b
1
, acc
1
, dac
1
i ∈ Norm∧
hd, c, e, b
2
, acc
2
, dac
2
i ∈ Norm∧
orthogonal(b
1
, b
2
)) →
conflict(hob, c, e, b
1
, acc
1
, dac
1
i,
hd, c, e, b
2
, acc
2
, dac
2
i))
where d ∈ {ob, pe}, c ∈ C, e ∈ E, b
1
and b
2
∈ B, acc
i
and dac
i
∈ Cnd, acc
1
≥ dac
2
and dac
1
≥ acc
2
.
In any other case, there is not a conflict. One must
observe that a prohibition norm applied to an action
ac will never conflict with another norm applied to an
action ac
′
orthogonal to ac. If ac is prohibited, the
entity can execute ac
′
.
6.4 Action Dependency
If a dependency relationship is defined between two
actions ac
1
and ac
2
then ac
1
must be executed before
ac
2
. If the client action is not executed, the dependent
action cannot be executed. The state that is the pre-
condition of the dependent action can only be achieve
IndirectNormativeConflict-ConflictthatDependsontheApplicationDomain
457

by executing the client action, i.e., the effect of exe-
cuting the client action is a subset of the precondition
of the dependent action.
In order to exemplify such relationship, let’s con-
sider again the example of governing a multi-agent
systems. The action to applySanctions depends on
the action to findOutViolations.
Definition 20 (Dependency). Relationship dependent
relates an action (called client) to another (called de-
pendent).
dependent ⊆ Action× Action
Definition 21 (Dependency Conflict Rule). If there is
a prohibition applied to any client action and an obli-
gation or permission applied to a dependent action
then these norms are in conflict.
∃b
1
∃b
2
((hpr, c, e, b
1
, acc
1
, dac
1
i ∈ Norm∧
hd, c, e, b
2
, acc
2
, dac
2
i ∈ Norm∧
dependent(b
1
, b
2
)) →
conflict(hpr, c, e, b
1
, acc
1
, dac
1
i,
hd, c, e, b
2
, acc
2
, dac
2
i))
where d ∈ {ob, pe}, c ∈ C, e ∈ E, b
1
and b
2
∈ B, acc
i
and dac
i
∈ Cnd, acc
1
≥ dac
2
,and dac
1
≥ acc
2
.
One must note that a prohibition applied to a de-
pendent action will neverconflict with any other norm
applied to its client action.
7 CONFLICT CHECKER
In this Section we present the algorithms that im-
plement OnCheckIn. As stated before in Section 1,
our approach is based on an ontology (available at
http://goo.gl/Qkutmo) that is used to describe the
norms by following definition 1 and all relationships
between contexts, entities and actions defined in pre-
vious sections. The onCheckIn algorithm assumes
that the ontology has been already parsed in two
groups, one containing all the norms and another all
the relationships. Thus, the onCheckIn algorithm re-
ceives as parameters the set of norms and the set of
relationships defined in the domain ontology. The al-
gorithm starts by calling the propagation algorithms
contextPropagation and entityPropagation.
Propagation algorithms implement the propaga-
tion restrictions defined in Sections 3 and 4 as rules
that generate new norms from the ones defined at
the level of composite elements such as an environ-
ment or organization. They assume that a given on-
tology defines only the norms applied to the more
general contexts or entities. The ontology designer
does not have to make explicit all the norms applied
to more concrete contexts or entities. It is the aim of
OnCheckIn to propagate the norms by following the
relationships between contexts and entities.
After propagating the norms, for each pair of
norms defined over the same context and that govern
the behavior of the same entity, the onCheckIn algo-
rithm checks if the norms are active at the same time,
given their activation and deactivation conditions. If
two norms are active, there may be a conflict if the
behavior being governed is the same (in the case of
direct conflicts) or if they are related by one of the ac-
tion relationships described in Section 6 (in the case
of indirect conflicts). The conflict rules are imple-
mented in Algorithms 4 to 7.
Our approach can be easily extended in two direc-
tions:
1. other relationships between contexts and entities
can be defined together with their propagation
rules. It is necessary to extend Algorithms 1
and 2;
2. other action relationships can be created together
with their conflict rules. It is necessary to define a
new conflict rule algorithm to deal with each new
relationship and include a call to this new algo-
rithm in Algorithm 1.
Algorithm 1: OnCheckIn.
Require: N : set of norms, R : set of relationships
1: function ONCHECKIN(N, R)
2: N = CONTEXTPROPAGATION(N)
3: N = ENTITYPROPAGATION(N)
4: conflict = false
5: for all n
1
∈ N do
6: for all n
2
∈ N do
7: if (n1.c = n2.c) ∧ (n1.e = n2.e) then
8: if intersect(n
1
, n
2
) then
9: conflict ←
10: n
1
.b = n
2
.b ∨
11: REFINEMENTCR(n
1
, n
2
, N) ∨
12: COMPOSITIONCR(n
1
, n
2
, N) ∨
13: ORTHOGONALITYCR(n
1
, n
2
) ∨
14: DEPENDENCYCR(n
1
, n
2
)
15: return conflict
8 RELATED WORK
Checking normative conflicts is one of the main chal-
lenges on the development of normative multi-agent
systems. The literature is abundant on the identifica-
tion of such conflicts. Most of it focuses on check-
ing simple and direct conflicts, such as (Kollingbaum
ICEIS2015-17thInternationalConferenceonEnterpriseInformationSystems
458

et al., 2008b; Vasconcelos and Norman, 2009). Since
the identification of direct conflicts is not the focus of
this paper, we will discuss in this Section related work
on the identification of indirect conflicts.
Algorithm 2: ContextPropagation.
Require: N : set of norms
1: function CONTEXTPROPAGATION(N)
2: for all n ∈ N do
3: if (n.c.type = “env”) ∧ (n.e =
/
0) then
4: for all e ∈ INHABIT(n.c) do
5: n
1
←
6: hn.d, n.c, e, n.b, n.acc, n.daci
7: N = INCLUDENORM(n
1
, N)
8: if (n.c.type = “org”) then
9: for all o ∈ HIEARCHY(n.c) do
10: n
1
←
11: hn.d, n.c, o, n.b, n.acc, n.daci
12: N = INCLUDENORM(n
1
, N)
13: return N
Algorithm 3: EntityPropagation.
Require: N : set of norms
1: function ENTITYPROPAGATION(N)
2: for all n ∈ N do
3: if (n.e.type = “role”) then
4: for all e ∈ PLAY(n.c, n.e) do
5: n
1
←
6: hn.d, n.c, e, n.b, n.acc, n.daci
7: INCLUDENORM(n
1
, N) ∨
8: if (n.c.type = “org”) ∧ (n.e =
/
0) then
9: for all r ∈ OWNERSHIP(n.c) do
10: n
1
←
11: hn.d, n.c, r, n.b, n.acc, n.daci
12: INCLUDENORM(n
1
, N) ∨
13: return N
In (Cholvy and Cuppens, 1995; Gunay and
Yolum, 2013) the authors describe that norms are ap-
plied to roles and that agents can play different roles.
Similarly to our approach, they also consider a rela-
tionship called play between agents and roles when
checking for conflicts between the norms.
In (Gaertner et al., 2007; Garcia-Camino et al.,
2007) the conflict checker considers that a norm ap-
plied to an activity can be propagated to other activ-
ities. Moreover, (Garcia-Camino et al., 2007) takes
into account the composition relationship between ac-
tivities. Similarly, the conflict checker in (Kolling-
baum et al., 2008a; Vasconcelos et al., 2007) allows
for the composition relationship and also the delega-
tion relationship. In (Vasconcelos et al., 2009), the
Algorithm 4: RefinementCR.
Require: N : set of norms, n
1
and n
2
: two norms
1: function REFINEMENTCR(n
1
, n
2
)
2: if ((n
1
.d =
′
pr
′
) ∧ (n
2
.d = (“ob” ∨ “pe”)) ∧
3: REFINEMENT(n
1
.b, n
2
.b)) then
4: return true
5: if ((n
1
.d = (“ob”∨ “pe”)) ∧ (n
2
.d =
′
pr
′
) ∧
6: REFINEMENT(n
1
.b, n
2
.b)) then
7: for all a ∈ SUBACTIONSOF(n
1
.b) do
8: for all n ∈ N do
9: if ((n.b = a) ∧
10: (n.d 6= “pr”)) then
11: return false
12: return true
13: return false
Algorithm 5: CompositionCR.
Require: N : set of norms, n
1
and n
2
: two norms
1: function COMPOSITIONCR(n
1
, n
2
)
2: if ((n
1
.d = (“ob”∨“pe”))∧ (n
2
.d = “pr”)) ∧
3: COMPOSITION(n
1
.b, n
2
.b)) then
4: return true
5: if ((n
1
.d = “pr”)∧
6: (n
2
.d = (“ob”∨ “pe”))) ∧
7: COMPOSITION(n
1
.b, n
2
.b)) then
8: for all a ∈ PARTACTIONSOF(n
1
.b) do
9: for all n ∈ N do
10: if ((n.b = a) ∧
11: (n.d 6= (“ob”∧ “pe”))) then
12: return false
13: return true
14: return false
Algorithm 6: OrthogonalityCR.
Require: n
1
and n
2
: two norms
1: function ORTHOGONALITYCR(n
1
, n
2
)
2: if ((n
1
.d =
′
ob
′
) ∧ (n
2
.d = (
′
ob
′
∨
′
pe
′
)) ∧
3: ORTHOGONAL(n
1
.b, n
2
.b)) then
4: return true
5: return false
Algorithm 7: DependencyCR.
Require: n
1
and n
2
: two norms
1: function DEPENDENCYCR(n
1
, n
2
)
2: if ((n
1
.d = (
′
ob
′
∨
′
pe
′
)) ∧ (n
2
.d =
′
pr
′
) ∧
3: DEPENDENT(n
1
.b, n
2
.b)) then
4: return true
5: return false
IndirectNormativeConflict-ConflictthatDependsontheApplicationDomain
459

same authors have included in the conflict checker the
refinement relationship but they have only considered
the simplest case when a superaction is specified by
only one subaction. All these approaches take into
account only the relationships between actions on the
checking of indirect conflicts. We claim that they are
incomplete since they ignore relationships among en-
tities and contexts and that they are not able to find
out conflicts between norms that do not define contra-
dictory or contrary deontic modalities.
Besides considering the composition relationships
between actions, the work in (Fenech et al., 2009)
also consider that there may be norms applied to the
same agent regulating contradictory or orthogonal ac-
tions. Similarly, in (Oren et al., 2008; Giannikis
and Daskalopulu, 2009) the authors consider conflicts
between obligations applied to the same agent that
govern actions that cannot be executed at the same
time. Although these approaches are able to consider
conflicts between norms that have the same deontic
modality, they do not consider the relationships be-
tween contexts and entities and they focus only on one
or two relationships between actions.
Approaches such as (Dung and Sartor, 2011; Li
et al., 2013a; Li et al., 2013b) focus on conflicts be-
tween norms defined in different contexts or in differ-
ent domains. These works are not related to ours since
we consider that the norms being checked are defined
in the same multi-agent system.
9 CONCLUSION
This work presents the identification of indirect nor-
mative conflicts, that is, conflicts that can only be
found when considering relationships of the domain.
Not only direct conflicts between pairs of norms,
but also indirect conflicts can be detected by the
OnCheckIn algorithm which it takes into account the
relationships among contexts, entities and behavior.
The relationships between contexts and entities
considered by the algorithm have been formalized to-
gether with their propagation constraints. We have
also formalized the relationships between actions and
defined rules to identify conflicts. As explained in
Section 7, our algorithm are extensible in order to
accommodate other relationships, propagation con-
straints and conflict rules can be defined.
A preliminary version of the conflict checker
(available at http://goo.gl/Qkutmo) was implemented
in Java using the OWL API library (Horridge and
Bechhofer, 2011). The user is able to load an OWL
ontology defining the norms and the relationships.
The conflict checker parsers the ontology and, for
each pair of norms, checks for conflicts by consid-
ering the relationships between the elements of these
norms. After the analysis, it informs the user about
the pairs of norms in conflict and the reason(s) of the
conflict, i.e., it informs if it is a direct conflict or an
indirect conflict camouflaged by a set of relationships
between the elements of the norms being analyzed.
One small difference between this preliminary ver-
sion and the OnCheckIn algorithm presented in this
paper is that it is not based on the propagation of
norms. Instead of propagating the norms according
to the relationships between contexts and entities, it
checks, for each pair of norms, if the contexts of the
norms are related and if the entities are related. Then
it checks if the behaviors are related. Although this
approach consumes less memory space than the one
presented in this paper (since it does not increase the
set of norms), it is less efficient since it must check,
for each pair of norms, if there are relationships be-
tween the contexts and relationships between the en-
tities of these norms.
We are in the process of adapting the preliminary
version of the conflict checker to include the prop-
agation algorithms discussed in Section 7. In addi-
tion, we are extending the OnCheckIn algorithm to
consider that norms can regulate the achievement of
states. Therefore, we are now working on describing
relationships between states and defining the connec-
tions between states and actions in order to be able
to check for conflicts among norms that regulate the
execution of an action and the achievement of a state.
We have not considered, in this version, the hierarchy
among entities such as roles. If such a relationship
was defined, it would have been possible to declare
a norm to a more general role and propagate such a
norm to more specific roles. As our approach is ex-
tensible, to accommodate this relationships is not a
difficult task.
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