Identifying Indirect Normative Conflicts using the WordNet Database
Jéssica Soares dos Santos
1
and Viviane Torres da Silva
2
1
Computer Science Department, Universidade Federal Fluminense, Niterói, Brazil
2
IBM Research (on leave from Universidade Federal Fluminense), Rio de Janeiro, Brazil
Keywords: Multi-agent Systems, Normative Conflicts, Conflict Detection, WordNet.
Abstract: A challenging issue in Multi-agent systems governed by multiple norms is to deal with normative conflicts,
which are situations where the fulfilment of a norm violates another one. There are several approaches in
the literature to detect conflicting norms. Some kinds of conflicts, here called direct conflicts, can be
detected through a simple comparison between the elements of the norms in order to check if they apply to
the same elements. For instance, if an obligation and a prohibition are applied to the same entity and govern
the same behavior in periods of time that intersects, these norms are in conflict. However, there are
conflicts, here called indirect conflicts, that can only be detected if the relationships among the elements of
the norms are taken into account. The majority of approaches that are able to detect indirect conflict
considers the relationships of the application domain that have been previously defined by the designer.
Different from those approaches, this paper focuses on the detection of indirect conflicts by taking into
account domain-independent relationships that have not been declared by the designer of the multi-agent
system. Our proposal searches for domain-independent relationships among the elements of the norms in the
WordNet database.
1 INTRODUCTION
In open Multi-Agent Systems (MASs) norms have
been applied as a means to restrict and guide the
behavior of autonomous and independently designed
software agents in order to avoid undesirable
behaviour. A norm is commonly associated with a
deontic concept (obligation, permission or
prohibition) and defines which actions an agent can
or cannot perform (Grossi et al., 2010). Due to the
numeral norms that may be necessary to govern a
normative MAS, conflicts between the norms may
arise. A conflict between two norms occurs when the
fulfilment of a norm violates the other norm. For this
reason, to guarantee the proper functioning of a
normative MAS, mechanisms for dealing with
normative conflicts are needed. A normative conflict
can be classified as:
• Direct Conflict: it involves two norms that are
associated with the same addressee, regulate the
same behavior and have opposite or contradictory
deontic modalities (obligation versus prohibition or
permission versus prohibition). This kind of conflict
can be detected through a simple analysis of the
norm elements (i.e., the addressee, behaviour and
context of the norm). Thus, a direct normative
conflict arises, for instance, when a norm obliges an
agent to perform an action in a given organization
and another norm prohibits the same addressee to
execute the same action in a same organization.
• Indirect Conflict: it involves two norms whose
elements are not the same but are related. It can only
be detected when relationships among elements of the
norms are identified. The deontic concepts associated
with the norms involved in an indirect conflict can be
opposite, contradictory or equal. For instance, an
indirect normative conflict arises between two norms
when both norms are addressed to the same agent and
one is prohibiting the execution of an action and the
other is obligating the execution of another action that
is a specialization of this action. Since the more
general action are being prohibited the more concrete
action cannot be executed.
The aim of our research is to develop a
mechanism able to detect indirect normative
conflicts by using the WordNet database (Miller,
1995) to identify the domain-independent
relationships among the elements of the norms.
WordNet is a public lexical database that stores
relationships among words.
The main difference between our approach and
others also able to detect indirect conflicts is that our
186
Santos, J. and Silva, V.
Identifying Indirect Normative Conflicts using the WordNet Database.
In Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016) - Volume 2, pages 186-193
ISBN: 978-989-758-187-8
Copyright
c
 2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

approach detects domain-independent relationships.
Other approaches, such as (da Silva and Zahn, 2014;
Aphale et al., 2012; Sensoy et al., 2012), focus on
the identification of domain-dependent relationships
that have been defined by the application designer.
In fact, our approach and the ones published in the
literature are complementary since the identification
of all kinds of indirect conflicts can only be detected
if both kinds of relationships are considered.
This paper is organized as follows. Section 2
presents related work. Section 3 describes the kinds
of relations that are defined in the WordNet
database. Section 4 presents the norm definition
adopted in this paper. Section 5 describes the kinds
of relationships explored by our approach and
formalizes the rules for conflict detection. Section 6
presents the detection algorithm and describes our
mechanism. Finally, Section 7 concludes and
presents some future work.
2 RELATED WORK
Several techniques have been proposed to detect
normative conflicts. Some of them only are able to
detect direct conflicts (Li et al., 2014; Vasconcelos
et al., 2012; dos Santos Neto et al., 2013; Gunay and
Yolum, 2013) while others can also detect indirect
conflicts (da Silva and Zahn, 2014; Aphale et al.,
2012; Sensoy et al., 2012; Fenech et al., 2009;
Giannikis and Daskalopulu, 2011). Each approach
presents a different definition for norms and can
detect different kinds of normative conflicts. The
majority of approaches to detect indirect conflicts
only deals with relationships among actions, i.e.,
they do not consider relationships between other
elements of the norm.
Since our approach focuses on the detection of
indirect conflicts, we will present a brief description
about the kinds of relationships that related
approaches consider in order to detect indirect
normative conflicts. Note that none of the
approaches are able to detect domain-independent
relationships. All of them assume that the domain-
dependent relationship are provided to the conflict
checker in order to check for conflicts.
2.1 Action Relationships
This subsection describes the action relationships
that have been found in the literature.
• Refinement: it defines a relation of specialization
among actions, i.e., it relates a sub action to a super
action. It is described in the following approaches
(da Silva and Zahn, 2014; Zahn, 2015);
• Composition: it defines a relation of composition
among actions, i.e., it determines that an action is
composed of another one. It is described in (da Silva
and Zahn, 2014; Zahn, 2015; Vasconcelos et al.,
2009; Aphale et al., 2012; Sensoy et al., 2012);
• Orthogonality: it defines actions that cannot be
performed simultaneously by the same entity, as
presented in (da Silva and Zahn, 2014; Zahn, 2015;
Fenech et al., 2008, 2009; Giannikis and
Daskalopulu, 2011, 2009);
• Dependency: it defines actions that are
preconditions of other actions. It is described in the
following approaches (da Silva and Zahn, 2014;
Zahn, 2015; Aphale et al., 2012; Sensoy et al.,
2012);
• Actions’ Side Effects: it determines the side-
effects of the execution of an action, as detailed in
(Aphale et al., 2012; Sensoy et al., 2012;
Kollingbaum and Norman, 2004; Kollingbaum et al.,
2006; Kollingbaum and Norman, 2006;
Kollingbaum et al., 2007).
Additionally, the approach presented in
(Vasconcelos et al., 2009) also can detect normative
conflicts that occur due to the delegation of tasks
among agents. Moreover, the work in (Zahn, 2015)
is able to analyze relationships (such as refinement,
dependency, orthogonality and composition)
between: (i) two actions; (ii) two states; or (iii) an
action and a state.
2.2 Entity Relationships
This subsection describes the entity relationships
that have been found in the literature.
• Play: it relates an agent to the roles it can play. It
is described in the following approaches (da Silva
and Zahn, 2014; Zahn, 2015; Cholvy and Cuppens,
1995, 1998);
• Play-in: it relates an agent to the organization
where it is playing a role, as presented in (da Silva
and Zahn, 2014;);
• Ownership: it relates roles to the organizations
where they were defined and can be played, as
detailed in (da Silva and Zahn, 2014; Zahn, 2015);
• Hierarchy: it relates a sub role to a super role. It is
defined in (da Silva and Zahn, 2014; Zahn, 2015).
2.3 Context Relationships
This subsection describes the context relationships
that have been found in the literature. The context of
Identifying Indirect Normative Conflicts using the WordNet Database
187

a norm represents the scope where it is defined, that
is, where the norm must be applicable. For instance,
a norm can be applied to an environment or to a
given organization.
• Inhabit: it relates an entity to the environment that
is its habitat. It is described in the following
approaches (da Silva and Zahn, 2014; Zahn, 2015);
• Hierarchy: it relates a sub context to its super
context, as presented in (da Silva and Zahn, 2014;
Zahn, 2015).
3 WORDNET
The WordNet is a lexical database that stores
relationships among words, which can be nouns,
verbs, adjectives and adverbs. WordNet groups
words that share the same meaning in a given
context. Each group of synonymous words is called
synset. A synset is the building block of WordNet
and all words in a synset denote the same concept.
Each synset has a brief description (called “gloss”)
of its meaning and may be associated with short
sentences that exemplify the use of synset members.
For instance, the nouns “error” and “mistake” are
grouped in a same synset whose gloss is “part of a
statement that is not correct”. Synsets are interlinked
through semantical and lexical relations. We will use
WordNet to identify relationships among the
elements of the norms that have not been defined in
the domain application. The relations that are stored
in WordNet are described in the next subsection.
3.1 WordNet Relations
WordNet defines six relationships, as follows:
• Synonymy: it is the main relation of WordNet.
Synonymous are words with the same meaning in a
given context. As stated in the beginning of this
Section, in WordNet words are grouped into synsets
(synonymous sets) and all words of a synset denote
the same concept. A word may appear in more than
one synset if it is associated with different meanings.
This relation is defined to nouns, verbs, adjectives
and adverbs.
• Hyponymy/Hypernymy: it relates a subset to a
superset since it links one synset more general to
another one more specific. For instance, whale is a
hyponym of mammal because whale is a kind of
mammal. Similarly, mammal is a hypernym of
whale. This relationship is similar to hierarchy
described in Section 2. This relation is defined to
nouns.
• Meronymy/Holonymy: it corresponds to the
relation part-whole. For instance, seat is a meronym
of chair because seat is part of chair. Similarly,
chair is a holonym of seat. This relation is only
defined to nouns.
• Entailment: it defines that a verb entails another
one. This relation is unilateral. For instance, to buy
entails to pay because to buy we need to pay. Then,
if two verbs are related by an entailment relation and
the event denoted by the first verb occur, the action
denoted by the second verb will also occur. This
relation is only defined to verbs.
• Antonymy: it relates a word to another one with
opposite meaning. For instance, clean is antonym of
dirty and vice-versa. This relation can be regarded a
special case of the orthogonality cited in Section 2.
This relation is defined to nouns, verbs and adverbs.
• Troponymy/Hypernymy: it relates a verb
denoting an action to another one denoting a manner
of doing this action. For instance, to lisp is a manner
of to talk. Thus, to lisp is a troponym of to talk. This
relationship is similar to refinement described in
Section 2. This relation is only defined to verbs.
4 NORM DEFINITION
The norm definition used in this paper is based on
the definition presented in (Zahn, 2015). We
consider that norms oblige, prohibit or permit an
entity to perform an action in a given context during
a period of time. Additionally, we consider that
agents inhabit environments and play roles in
organizations.
Our definition of norm is more expressive than
the one described in (Zahn, 2015) because we extend
it including an optional field “obj”, which
corresponds to a parameter of the action being
regulated by the norm that describes an object
applied to the action. In order to exemply, we can
consider the following actions: dress(skirt) and
eat(banana).
A norm is a tuple of the form:
Norm = 〈id, d, c, e, act (obj), ac, dc〉,
where id is the norm identifier, d is a deontic
concept from the set {obligation, permission,
prohibition}; c ∈ C is the context where the norm is
defined (i.e., an organization org ∈ O or an
environment env ∈ Env.); e ∈ E is the entity whose
action is being regulated by the norm (i.e., an agent
a ∈ A, an organization org ∈ O or a role r ∈ R. We
use the symbol “_” to determine that a norm is
addressed to all entities of a given context); act ∈
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Act is the action being regulated; obj ∈ Obj is an
object associated with the action (it is an optional
field. We will use the symbol “_” to represent that
an action is not associated with any object.); ac ∈
Cnd is the condition that activates the norm; dc ∈
Cnd is the condition that deactivates the norm. These
conditions are dates represented in the format:
MM/dd/yyyy HH:mm:ss.
5 KINDS OF RELATIONSHIPS
In this section we present the relationships that our
proposal will analyze in order to detect indirect
normative conflicts. First, the elements of a norm are
mapped to nouns or verbs and then we identify the
relationships among such elements.
We adopt the norm propagation method
described in (Zahn, 2015; da Silva et al., 2015),
which determines propagation rules to create new
norms in order to facilitate the detection process.
Propagation rules propagate norms according to the
relationships between entities or contexts. After the
norm propagation, the detection method only need to
verify, for each pair of norms addressed to the same
entity and context, if the actions regulated by the
norms are related. To avoid the unnecessary
propagation of norms addressed to synonymous
entities/contexts, we identify and locally rewrite
synonymous entities/contexts before propagating
norms. We use First-order logic (Smullyan, 1995) to
define norm propagation constraints. For the sake of
simplicity, we omit the norm identifier id.
Since our norm definition (Section 4) considers
that an action may be applied over an object, norms
associated with related actions only can conflict with
each other if both actions are associated with the
equivalent objects (i.e., synonyms or equal) or they
are not associated with objects.
The identification of relationships and the
propagations do only consider the entities, contexts,
and actions defined in the set of norms being
considered.
5.1 Context Relationships
In this subsection we describe the context
relationships that can be captured by our approach.
All contexts are mapped to nouns and the
relationships defined in the WordNet that applied to
nouns are investigated. We do not consider the
antonymy relationship because there is not a case
where a norm must be propagated due to an
antonymy relation between contexts.
• Synonymy Among Contexts: we use the
WordNet relation “Synonymy” to identify
synonymous contexts. For instance, the context USA
is equivalent to the context United States of
America.
Rewriting Rule: Synonymous contexts are locally
rewritten in order to unify the norm elements that
are equivalent. Thus, we iterate over the set of
contexts used in the set of norms and create a list
where each item is a pair of synonyms contexts.
After that, we replace each context for its synonym
stored in the synonyms list. For instance, if there is
a norm n1 that is associated with the context USA
and there is a norm n2 associated with the context
United States of America, since both contexts are
synonyms, norm n2 is locally rewritten to be
associated with the context USA.
•
Specialization Among Contexts: we use the
WordNet relation “Hyponymy/ Hypernymy” to
identify related contexts. For instance, the context
Brazil is a hyponym of the context South America.
Propagation Rule: If a norm is addressed to an
organization and does not specify a specific entity,
so it must be addressed to all its sub organizations.
∃o
1
(〈d, o
1
, _, act (obj), ac, dc〉 ∈ Norm →
∀o
2
(HyponymyHypernymy (o
2
, o
1
) →
〈d, o
1
, o
2
, act (obj), ac, dc〉 ∈ Norm))
where d ∈ {obligation, permission, prohibition}; o
1
, o
2
∈
O; act ∈ Act; obj ∈ Obj; ac ∈ Cnd; dc ∈ Cnd.
• Part-whole Among Contexts: we use the
WordNet relation “Meronymy/ Holonymy” to
identify related contexts. For instance, the context
intensive care unit (ICU) is part of the context
hospital.
Propagation Rule: If a norm is addressed to an
organization and does not specify a specific entity,
it must be addressed to all organizations that are
part of the given organization.
∃o
1
(〈d, o
1
, _, act, ac, dc〉 ∈ Norm →
∀o
2
(MeronymyHolonymy (o
2
, o
1
) →
〈d, o
1
, o
2
, act, ac, dc〉 ∈ Norm))
where d ∈ {obligation, permission, prohibition}; o
1
, o
2
∈
O; act ∈ Act; obj ∈ Obj; ac ∈ Cnd; dc ∈ Cnd.
5.2 Entity Relationships
In this subsection we describe the entity
relationships that can be captured by our approach.
All entities are mapped to nouns and the
relationships defined in the WordNet that applied to
nouns are investigated. We do not consider the part-
whole relationship among entities because we did
not find cases where an entity is composed of parts.
Our approach can be easily extended to support the
Identifying Indirect Normative Conflicts using the WordNet Database
189

relationship part-whole among entities if necessary.
We also do not consider the relationship antonymy
among entities because it does not imply that a norm
must be propagated.
• Synonymy Among Entities: we use the WordNet
relation “Synonymy” to identify synonymous
entities. For instance, the entity child is equivalent to
the entity kid.
Rewriting Rule: Synonymous entities are locally
rewritten in order to unify the norm elements that
are equivalent. This process is similar to the one
described in the “synonymy among contexts”, in
Section 5.1.
• Specialization Among Entities: we use the
WordNet relation “Hyponymy/ Hypernymy” to
identify related entities. For instance, the role
angiologist is hyponym of the role doctor.
Propagation Rule: If a norm is addressed to a
hypernym entity (super role), then it must apply to
all sub roles of the given super role. Only the roles
defined in the domain ontology are compared.
∃r
1
(〈d, c, r
1
, act (obj), ac, dc〉 ∈ Norm →
∀r
2
(HyponymyHypernymy (r
2
, r
1
) →
〈d, c, r
2
, act (obj), ac, dc〉 ∈ Norm))
where d ∈ {obligation, permission, prohibition}; c ∈ C; r
1
,
r
2
∈ R; act ∈ Act; obj ∈ Obj; ac ∈ Cnd; dc ∈ Cnd.
5.3 Action Relationships
In this subsection we describe the action
relationships that can be captured by our approach.
All actions are mapped to verbs and the relationships
defined in the WordNet that applied to verbs are
investigated.
• Synonymy Among Actions: we use the WordNet
relation “Synonymy” to identify that an action is
equivalent to another one. For instance, the actions
to cooperate and to collaborate are synonyms.
Conflict Rule: Norms associated with
synonymous actions are in conflict if one is a
prohibition and the other is a permission or
obligation, and both actions are applied to the
equivalent objects (synonym or equal) or they are
not associated with objects.
∃act
1
∃act
2
((〈prohibition, c, e, act
1
(obj
1
), ac
1
,dc
1
〉 ∈ Norm
∧ 〈d, c, e, act
2
(obj
1
), ac
2
, dc
2
〉 ∈ Norm
∧ (Synonymy (act
1
, act
2
)) →
conflict (〈prohibition, c, e, act
1
(obj
1
), ac
1
, dc
1
〉,
〈d, c, e, act
2
(obj
1
), ac
2
, dc
2
〉))
where d ∈ {obligation, permission}; c ∈ C; e ∈ E; act
1
,
act
2
∈ Act; obj
1
∈ Obj; ac
1
, ac
2
∈ Cnd; dc
1
, dc
2
∈ Cnd; dc
2
≥ ac
1
; and dc
1
≥ ac
2
.
• Specialization Among Actions: we use the
WordNet relation “Troponymy/Hypernymy” to
identify related actions. For instance, the action to
talk is a troponym of the action to communicate.
Conflict Rule: Norms associated with
specialization actions are in conflict if the super
action (hypernym) is prohibited and the sub action
(troponym) is permitted or obliged, and both
actions are applied to equivalent objects (synonym
or equal) or they are not associated with objects.
∃act
1
∃act
2
((〈prohibition, c, e, act
1
(obj
1
), ac
1
, dc
1
〉 ∈
Norm
∧ 〈d, c, e, act
2
(obj
1
), ac
2
, dc
2
〉 ∈ Norm
∧ TroponymyHypernymy (act
2
, act
1
)) →
conflict (〈prohibition, c, e, act
1
(obj
1
), ac
1
, dc
1
〉,
〈d, c, e, act
2
(obj
1
), ac
2
, dc
2
〉))
where d ∈ {obligation, permission}; c ∈ C; e ∈ E; act
1
,
act
2
∈ Act; obj
1
∈ Obj; ac
1
, ac
2
∈ Cnd; dc
1
, dc
2
∈ Cnd; dc
2
≥ ac
1
; and dc
1
≥ ac
2
.
•
Entailment: we use the WordNet relation
“Entailment” to identify related actions. For
instance, the action to buy entails the action to pay.
Conflict Rule:
Norms associated with entailment
actions are in conflict if the entailed action is a
prohibition and the other action is an obligation or
permission, and both actions are applied to
equivalent objects (synonym or equal) or they are
not associated with objects.
∃act
1
∃act
2
((〈d, c, e, act
1
(obj
1
), ac
1
, dc
1
〉 ∈ Norm
∧ 〈prohibition, c, e, act
2
(obj
1
), ac
2
, dc
2
〉 ∈ Norm
∧ Entailment (act
1
, act
2
)) →
conflict (〈d, c, e, act
1
(obj
1
), ac
1
, dc
1
〉,
〈prohibition, c, e, b
2
(obj
1
), ac
2
, dc
2
〉))
where d ∈ {obligation, permission}; c ∈ C; e ∈ E; act
1
,
act
2
∈ Act; obj
1
∈ Obj; ac
1
, ac
2
∈ Cnd; dc
1
, dc
2
∈ Cnd; dc
2
≥ ac
1
; and dc
1
≥ ac
2
.
• Antonymy Among Actions: we use the WordNet
relation “Antonymy” to identify that an action is
opposite to another one. For instance, the action to
enter is antonym of the action to leave.
Conflict Rule: Norms associated with antonym
actions are in conflict if they are obligations, and
both actions are applied to equivalent objects
(synonym or equal) or they are not associated with
objects. Note that a conflict does not occurs if the
norms are permissions or are prohibitions.
∃b
1
∃b
2
((〈obligation, c, e, act
1
(obj
1
), ac
1
, dc
1
〉 ∈ Norm
∧ 〈obligation, c, e, act
2
(obj
1
), ac
2
, dc
2
〉 ∈ Norm
∧ Antonymy (act
2
, act
1
)) →
conflict (〈obligation, c, e, act
1
(obj
1
), ac
1
, dc
1
〉,
〈obligation, c, e, act
2
(obj
1
), ac
2
, dc
2
〉))
where c ∈ C; e ∈ E; act
1
, act
2
∈ Act; obj
1
∈ Obj; ac
1
, ac
2
∈
Cnd; dc
1
, dc
2
∈ Cnd; dc
2
≥ ac
1
; and dc
1
≥ ac
2
.
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190

6 ALGORITHM
In this section we present pseudocodes and describe
our detection method. The WordNet Conflict
Checker
1
checkes for conflicts between a set of
norms by considering the relationships between their
elements (entities, contexts, actions) that are defined
in the WordNet database. The conflict checker was
implemented by using Java language. To execute
searches over the WordNet database, we use JWNL
(Walenz and Didion, 2011), a Java library that allow
us to connect the information from an offline
WordNet database to a Java program. The main
steps of our detection method (see Algorithm 1) are
described as follows
. Due to the limitation of space
we omit the algorithms of identification of
synonyms and norm propagation.
• Identify Synonymous: We use the WordNet
database (synonymy relationship) to identify the
synonymous contexts and entities and locally rewrite
the norms to unify the syntax of the norms and avoid
the propagation of equivalent norms. In this step we
store the pairs of synonymous entities/contexts in a
list. Each item of the list is a pair of the form (key,
value), where the key is the entity to be replaced for
the entity stored in the value. For instance, if the
domain ontology has the entities: child, kid, minor,
the algorithm will iterate sequentially through the
entities and create the list: {(kid, child), (minor,
child)}. Then, norms addressed to the entity kid or to
the entity minor will be locally rewritten to be
addressed to the entity child.
• Norm Propagation: The second step of the
algorithm is to propagate norms from more general
contexts (or entities) to more specific contexts (or
entities). This process uses the WordNet information
in order to identify the relationships hierarchy and
part-whole between contexts and entities. In this step
new norms are created according to the relations
identified. This step is described in Section 5.1 and
5.2.
• Get related Norms: In the third step, the detection
mechanism gets each pair of norms and verifies
whether the norms are addressed to the same entity,
to the same context and if their period of validity
(activation and deactivation conditions) intersect.
This step is described in Algorithm 1. After that, the
mechanism verifies if there is a direct conflict.
Otherwise, the mechanism uses the WordNet
database to verify if there is an indirect conflict (see
Algorithm 2).
1
Available at https://goo.gl/KeF5M3
• WordNet Conflict Rules: In the fourth step, the
conflict rules of the WordNet relationships (synonymy,
specialization, antonymy, entailment) are applied in
order to detect conflicts. These rules were specified in
Section 5. The algorithm analyzes the deontic concept
of the norms and searches for relationships among the
actions or objects using WordNet database. The
algorithms 3, 4, 5, 6 detail the application of conflict
rules. In our implementation available to download, the
program informs to the user whether each pair of
compared norms are in conflict or not and presents the
reason of the conflict. For the sake of simplicity,
Algorithm 1 only informs to the user whether there is a
normative conflict or not.
So, in summary, the conflict checker algorithm
uses the WordNet database in order to: (i) unify the
syntax of the elements defined in the set of norms;
(ii) propagate norms addressed to more general
entities (or contexts) to more specific entities (or
contexts); and (iii) search relationships between the
actions defined in the set of norms to detect possible
indirect normative conflicts.
Algorithm 1: Check Conflicts.
Require: N: set of norms, E: set of entities, C: set of contexts,
W: WordNet database
function EXECUTE (N,
E
,
C
,
W
)
conflictTime
← false
conflictAction
← false
synonymEntities
← GETSYNONYMENTITIES (E, W)
synonymContexts
← GETSYNONYMCONTEXTS (C, W)
N
← PROPAGATEENTITY (N, synonymEntities, E, W)
N
← PROPAGATECONTEXT (N, synonymContexts,
synonymEntities, E, W)
for all n1
∈ N do
for all n2 ∈ N do
conflictTime
← timeIntersect (n1, n2)
if ((conflictTime = true) ∧
(n1
.
e = n2
.
e) ∧
(n1
.
c = n2.c)) then
conflictAction
← CHECKACTION (n1, n2, W)
return conflictAction
Algorithm 2: Check Action.
Require: n1, n2: norms, W: WordNet database
function CHECKACTION (n1, n2, W)
if ((n1.ac
t
= n2.ac
t
) ∧ (n1.ob
j
= n2.ob
j
) ∧
(n1.
d
= “FORBIDDEN”) ∧
(n2.
d
= (“OBLIGED” ∨ “PERMITTED”))) then
return true
if (SYNONYMY
CR (n1, n2, W) ∨
ANTONYMY
CR (n1, n2, W) ∨
SPECIALIZATION
CR (n1, n2, W) ∨
ENTAILMENT
CR (n1, n2, W) then
return true
return false
Identifying Indirect Normative Conflicts using the WordNet Database
191

Algorithm 3: Synonymy Conflict Rule.
Require: n1, n2: norms, W: WordNet database
function SYNONYMYCR (n1, n2, W)
if (SYNONYMY (n1.ac
t
, n2.ac
t
,
W
) ∧
((n1.obj = n2.obj) ∨ SYNONYMY (n1.obj, n2.obj, W)) ∧
(n1.d = “FORBIDDEN”) ∧
(n2.
d
= (“OBLIGED” ∨ “PERMITTED”))) then
return true
return false
Algorithm 4: Antonymy Conflict Rule.
Require: n1, n2: norms, W: WordNet database
function ANTONYMYCR (n1, n2, W)
if (ANTONYMY (n1.act, n2.ac
t
,
W
) ∧
((n1.obj = n2.obj) ∨ SYNONYMY (n1.obj, n2.obj, W)) ∧
(n1.d = ”OBLIGED”) ∧ (n2.d = “OBLIGED”)) then
return true
return false
Algorithm 5: Specialization Conflict Rule.
Require: n1, n2: norms, W: WordNet database
function SPECIALIZATIONCR (n1, n2, W)
if (
TROPONYMHYPERNYM (n2.act, n1.act, W) ∧
((n1.obj = n2.obj) ∨ SYNONYMY (n1.obj, n2.obj, W)) ∧
(n1.d = “FORBIDDEN”) ∧
(n2.d = (“OBLIGED” ∨ “PERMITTED”))) then
return true
return false
Algorithm 6: Entailment Conflict Rule.
Require: n1, n2: norms, W: WordNet database
function ENTAILMENT
CR (n1, n2, W)
if (ENTAILMENT (n1.act, n2.act, W) ∧
((n1.obj = n2.obj) ∨ SYNONYMY (n1.obj, n2.obj, W)) ∧
(n2.d = “FORBIDDEN”) ∧
(n1.d = (“OBLIGED” ∨ “PERMITTED”))) then
return true
return false
7 CONCLUSIONS
The detection of normative conflicts is an essential
key in a MAS governed by multiple norms. In the
literature, there are several approaches that deal with
conflicts among norms. The majority is only able to
detect direct conflicts, but other ones can also detect
indirect normative conflicts. The kinds of
relationships that can be identified are different
according to each approach. For the best of our
knowledge, all approaches described in the literature
only can detect indirect normative conflicts when the
designer explicitly defines relationships among
entities, contexts, actions and states. However, there
are relationships that are independent of the domain.
Our research focuses on the detection of indirect
normative conflicts using relationships already
defined in WordNet database to identify
relationships among elements of the norms. We
formalize the relationships explored and describe
rules to detect normative conflicts. Our approach can
detect relationships among the elements of the
norms without analyzing relationships of the domain
application. In order to improve our mechanism and
make it more complete, we are studying the
possibility of extending our approach to capture
others relationships. We are in process of integrating
detection methods that analyze domain application
relationships.
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