NC4OMAS: A Norms-based Approach for Open Multi-Agent

Systems Controllability

Mohamed Sedik Chebout

, Farid Mokhati

and Mourad Badri

Department of Mathematics and Computer Science, ReLa(CS)2 Laboratory, University of Oum el Bouaghi,

Oum el Bouaghi, Algeria

Department of Mathematics and Computer Science, Software Engineering Research Laboratory, University of Quebec,

Trois-Rivières, Canada

Keywords: Controllability, Norms, Open Multi-Agent Systems, AGR Model, JAVA Expert System Shell,

Aspect-Oriented Programming.

Abstract: Normative Open Multi-Agent Systems (NOMAS) are systems in which norms play a crucial role for

organizing, coordinating, controlling agents’ behaviours and interactions. In addition, heterogeneous agents,

in Open Multi-Agent Systems (OMAS), can work in similar or different ends. This might deviate the target

system state and lead to a chaotic behaviour. A particular kind of OMAS is implemented based on AGR

(Agent/Group/Role) model. This paper proposes a novel Norms-based Controllability approach for AGR-

based OMAS (NC4OMAS). Mainly, the proposed approach is divided into two phases: monitoring and

controlling. Aspect-Oriented Programming (AOP) technique is used for norm monitoring compliance. Also,

JAVA Expert System Shell (JESS) is used for norm specification, norm modification and for making

inference over norms at runtime. In order to address limitations and advantages of our approach, we

summarise the most relevant works on norms-based control according to some comparison criteria we

proposed.

1 INTRODUCTION

Open MAS (OMAS) are characterized, mainly, by the

heterogeneity of their participants (Criado et al., 2013),

the member agents are developed by different parties

and serve different, often competing interests (Artikis

et al., 2016). Unlike classical MASs, agents in OMAS

can freely join and leave systems at any time by

requesting and/or leaving roles. Accordingly, hetero-

geneous agents playing their roles in such systems

increase the risk to lead to non-desired situations,

unanticipated interactions and expand the gap between

the system observed

behaviour and the expected one

(Hewitt, 1991). To avoid that risk, it is necessary to

define control mechanisms to lead the system

behaviour from any unpredictable situation to a

predefined target state. As stated in (Hewitt, 1991):

“openness without control may lead to a chaotic

behaviour”.

https://orcid.org/0000-0001-5373-4495

https://orcid.org/0000-0003-4311-342X

https://orcid.org/0000-0001-9034-9713

Roughly, the concept of controllability denotes

the ability to move a system state with its entire

current configuration using only certain potential

manipulations (Liberty, 1972) (Sontag, 1998).

Controllability and observability are dual aspects of

the same problem. Observe (i.e., monitor) a given

system consists to delegate another system (a

monitor), which runs concurrently with the monitored

one, for providing detailed information about the

execution of the other program (ISO/IEC/IEEE,

2017) (Hammoud et al., 2016).

A special kind of OMAS is implemented using

AGR (Agent/group/role) model in which the internal

agent structure is not specified (i.e., agent

heterogeneity). Also, groups in AGR model are

considered as black boxes where what happens in a

group cannot be seen from agents that do not belong

to that group (Ferber & Gutknecht, 1998).

164

Chebout, M., Mokhati, F. and Badri, M.

NC4OMAS: A Norms-based Approach for Open Multi-Agent Systems Controllability.

DOI: 10.5220/0010793600003116

In Proceedings of the 14th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2022) - Volume 1, pages 164-171

ISBN: 978-989-758-547-0; ISSN: 2184-433X

This study is devoted to addressing the issue of

AGR-based OMAS controllability by proposing a

novel approach called NC4OMAS for Norms-based

Controllability for Open Multi-Agent Systems. In

NC4OMAS, norms specify the behaviours that agents

should follow to achieve the objectives of the OMAS.

JAVA Expert System Shell (JESS) (Friedman-Hill,

2008) is proposed, in the context of this paper, for

norm specification, norm updating and for making

inference over norms. Likewise, Aspect-Oriented

Programming (AOP) is used, in NC4OMAS, in order

to implement norm monitoring process. An initial

synthesis of OMAS control problem has been

introduced and investigated in (Chebout et al., 2016)

followed by an implementation of a dedicated

software tool for monitoring AGR-based OMAS in

(Chebout et al., 2019).

The rest of this paper is organized as follows:

Section 2 provides some related works about norms-

based control for OMAS. Section 3 outlines the main

preliminaries we used in our approach. Section 4

presents the proposed approach. Section 5 discusses

our proposal in light of comparisons made with some

relevant works. Finally, section 6 draws some

conclusions and gives some future work directions.

2 STATE OF THE ART

In the last two decades, several research works have

been published in which norms-based control related

concepts have been treated.

In (Criado et al., 2013), a distributed architecture

for enforcing norms in OMAS has been proposed

under the name of MaNEA (Magentix2 Norm-

Enforcing Architecture). The main aim of MaNEA is

to overcome problems of existing proposals on norm

enforcement. Also, MaNEA supports the creation and

deletion of norms on-line as well as the dynamic

activation and expiration of instances. MaNEA shows

good performance result in terms of: number of

instantiations, number of agents, number of roles,

number of norms, number of iterations and number of

actions compared to (Modgil et al., 2009).

In (Mahmoud et al., 2014), a literature review of

normative MAS has been established. That review

work classifies norms into two main categories:

conventional and essential norms. This latter,

encompasses three norm types: constitutive,

regulative, and procedural norms. A new norm type

has been proposed under the term: recommendation

norm. Authors contribute, also, with a norm lifecycle

process that summaries the different stages that affect

the norm from creation to removal.

In (Alechina et al., 2018), the problem of

detecting norm violations in OMAS is considered. In

that work, the MAS does not need to bear the cost of

paying for monitoring, as assumed in (Fagundes et al.,

2014). Agents are not always rewarded after they

monitor, but only if they discover a violation. A key

issue for that approach is how to incentivize the

agents to monitor the actions of other agents.

In (Fagundes et al., 2014), an approach for

analysing the trade-off between norm enforcement

efficiency and its cost has been proposed.

Furthermore, the cost is associated with norm

enforcement. For that, a simulation-based method to

calculate trade-offs involved in enforcement

mechanisms has been developed and experimented.

In that work, authors confirm that norm designers,

based on information provided by their simulation,

are able to analyse the trade-off between efficiency

and cost of norm enforcement.

In (Marir et al., 2019), an extension of JADE

agent platform (Bellifemine et al., 2007) named

Normative JADE (NorJADE) has been proposed to

support different aspects related to MAS normativity.

The proposed extension consists in providing JADE

developers with a normative framework in which

norm representation, norm enforcement, and norm

monitoring techniques are specified. Also, NorJADE

implements several norm related mechanisms using

AspectJ.

Although these works have considerably

forwarded the control issue in OMAS by proposing

novel approaches for each norm subareas (i.e., norm

lifecycle, conflict resolution between norms, norm

enforcement, and norm implementation), they did not

take into account the specificities of AGR-based

OMASs and they did not discuss, in a clear way, how

operationally norm compliance is monitored. It

should be pointed out that norm synthesis aspect is

not considered in this paper nor in the studied

literature. Although the enormous works in such

domain, using a rule-based system for expressing

norms is limited to a particular kind of system (i.e.,

electronic institutions). Norm enforcement proposed

architectures, in existing literature, are almost

centralized. However, a distributed architecture is

strongly preconized in order to avoid drawbacks

related to centralized ones (i.e., communication

overhead, etc.).

Existing works on norm monitoring in MAS,

except (Criado et al., 2013), delegate an agent for

monitoring norm compliance (second- and third-

party observability). Putting a particular agent in

charge for observing other agent’s behaviour is a

good solution in the design level. However,

NC4OMAS: A Norms-based Approach for Open Multi-Agent Systems Controllability

165

interaction between monitor agent and other agents

implied in control process will increase the amount of

communication and affect considerably the

performances of controlled system. To the best of our

knowledge, this work is the first that uses AOP

techniques to provide support for norm monitoring.

Also, norm modification issue was not treated as well.

3 PRELIMINARIES

In what follows, we introduce the main materials we

used to support our proposal.

3.1 Normative Open Multi-Agent

System

According to (Boella et al., 2008), a normative MAS

“is a multi-agent system organized by means of

mechanisms to represent, communicate, distribute,

detect, create, modify, and enforce norms, and

mechanisms to deliberate about norms and detect

norm violation and fulfilment”. Also, norms have

been incorporated into OMAS to express the expected

behaviour of agents.

In Normative OMAS (NOMAS) literature, most

used norms are those who use deontic logic operators

(i.e., regulative norms): obligations, prohibitions, and

permissions (von Wright, 2021) (Woleński, 2016). In

our work, the focus is on a special type of norms in

which temporal constraints are considered (i.e.,

conditional norms). Obligations, prohibitions, and

permissions are submitted to temporal constraints:

start time and deadline. For instance, obligation start

time describes the moment when the norm is

instantiated. However, obligation deadline means that

obligation does not produce any effects after this

time. The period between obligation start time and

deadline expresses the fact when obligation is in

force. In AGR-based OMAS, norms will be activated

when agent requests a role. This means that norms are

addressed to roles played by agents. In contrast, norm

is deactivated when an agent leaves a role whatever

norm is fulfilled or violated.

To address the norms-based control process in

OMAS, norms should be communicated to agents

newly integrated in the system and, as a consequence,

agents may decide not to comply with the norms

(Criado et al., 2013) (Mahmoud et al., 2014). In order

to deal with agent autonomy in which agents can

work toward similar or different goals, the step of

agent decision related to comply or not with norms

will be bypassed in this work.

3.2 AGR Model

Agent, Group and Role (AGR) Model, is a generic

organizational model of multi-agent systems.

According to (Ferber et al., 2003), an agent is an

active, communicating entity playing roles within

groups, a group is used as a context for a pattern of

activities. Also, a group is defined as a set of agents

sharing some common characteristics. A role is the

abstract representation of a functional position of an

agent in a group. An agent must play a role in a group,

but an agent may play several roles (figure 1).

Figure 1: AGR core model (Ferber & Gutknecht, 1998).

Therefore, MaDKit (Multi-agent Development Kit)

platform (Gutknecht & Ferber, 2001) consists of an

operationalization of the AGR model and is selected

in this work for proposed approach implementation

purposes. Under MaDKit, an agent that wants to get

in the system, should pass an explicit request via

requestRole primitive. In contrast, agents that want to

go out from the system use leaveRole primitive.

3.3 JESS

Java Expert System Shell (JESS) is an editor of expert

systems and scripting language from Sandia National

Laboratories, written entirely in JAVA and using a

Lisp-like notation (Friedman-Hill, 2008). JESS

supports the development of rule-based systems that

can be tightly coupled to code written in JAVA

(Garcia-Camino et al., 2005). There are three ways to

represent knowledge in JESS: rules, functions and

Object-Oriented Programming (Friedman-Hill,

2003). Also, JESS uses backward chaining inference

method. A typical rule-based system has, at least,

three basic components: fact-list (i.e., instance-list),

knowledge-base (i.e., rule-base) and inference

engine. By using JESS, JAVA functions may be

called from JESS code, extending JESS by writing

JAVA code and embedding JESS in JAVA

Application (Friedman-Hill, 2008).

This feature adds more flexibility to the code.

JESS is used, in the context of this work, for

specifying and making inference over norms.

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Figure 2: NC4OMAS architecture.

3.4 AspectJ

AspectJ is the most common AOP implementation for

JAVA (Kiczales et al., 2001). AspectJ supports the

definition of aspects, advices, join points, and

pointcuts. An advice is a special method-like

construct attached to join points. Join points are well-

defined points in the structure and dynamic execution

of a system. Examples of join points are method calls,

method executions, etc. Pointcuts are collections of

join points and are used in advice definitions. An

aspect defines sets of pointcuts and advices (Kiczales

et al., 2001). AspectJ provides an effective way for

monitoring agent movements and norm compliance.

4 PROPOSED APPROACH

The main purpose of Norms-based Controllability for

Open Multi-Agent Systems (NC4OMAS) is to

control AGR-based OMASs in order to guide, based

on a norm-driven process, their behaviour for

achieving expected states. To this end, NC4OMAS

approach is divided into two main phases (figure 2):

observing (i.e., monitoring) phase and controlling

phase.

4.1 Observing Phase

The monitoring phase consists of tracking and

gathering AGR related information. System

monitoring module in NC4OMAS architecture is

implemented as a set of AspectJ aspects. Based on

AspectJ pointcuts, system monitoring tracks agent’s

entrance, departure and performed actions. Snippet 1

shows a piece of code of system monitoring aspect in

which a pointcut named observeRequiredRole

intercepts all calls to requestRole primitive. After

that, a particular list named activatedRequested

RoleList will be updated. activatedRequested

RoleList encompasses information about agentID and

assigned roles.

pointcut observeRequiredRole(String

communityName, String groupName, String

roleName, Object passKey) :

call(ReturnCode *.requestRole(..)) &&

args(communityName, groupName, roleName,

passKey);

after (String communityName, String

groupName, String roleName, Object

passkey) returning (Returncode r) :

observeRequiredRole(communityName,

groupName, roleName, passKey){

AbstractAgent ag = (AbstractAgent)

thisJoinPoint.getTarget();

…

if (r.equals(ReturnCode. SUCCESS)){

activatedRequestedRolelist.put(agAd.getN

etworkID(), roleName);}

}

Snippet 1: Monitoring the agent requested roles.

NC4OMAS GUI

NC4OMAS: A Norms-based Approach for Open Multi-Agent Systems Controllability

167

4.2 Controlling Phase

The purpose of NC4OMAS controlling phase is to

delegate a third-party MAS in order to ensure, for

OMAS, the achievement of expected state. Also,

delegated MAS is composed by a set of agents in

which their tasks are related to norm lifecycle. In this

work, four essential norm lifecycle phases have been

adopted: norm creation, norm instantiation, norm

monitoring, and norm enforcement. In Norm creation

step, a set of domain dependent norms will be created

and saved in norm database before launching the

controlled system (i.e., offline norm design). Norm

instantiation and norm enforcement steps are

delegated to specific agents named: norm instantiator

and norm enforcer respectively. Also, norms

monitoring step is implemented by a specific agent

that uses AspectJ constructors for observing norm

compliance. A particular agent type called norm

manager consists of supervising several norm related

tasks assigned to norm instantiator and norm

enforcer. Delegated MAS entities are dispatched over

groups of agents of the OMAS submitted to control.

Likewise, norm manager, norm instantiator, and norm

enforcer agents should pass, every one, an explicit

request for performing norm management, norm

instantiation and norm enforcement roles respectively

in each created group.

In contrast, norm instantiation consists of making

a copy of created norm (i.e., from norm database) that

corresponds to requested role using JESS pattern

matching mechanism. Instantiated norm for a given

role is taken based on JESS inference engine

following two successive steps: selection and

filtering which will be proceeded based on JESS

built-in RETE algorithm (Forgy, 1982). Once norm is

instantiated (i.e., in force), it will be inserted in a

particular list named instantiatedNormList.

Conversely, norm expiration process consists of

removing instantiated norms from the enforcement

process when a given agent gets out of the system.

For that, a specific list named desactivatedNormList

is maintained.

In NC4OMAS, a norm is specified as JESS rule.

This latter is similar to an IF-THEN statement. Rules

have two parts a left-hand side (LHS) and a right-

hand side (RHS) separated by the connective (=>).

The LHS is employed for matching fact patterns

based on RETE algorithm. Snippet 2 shows a

prohibition norm named AuthorProhibitionRule

related to paper submission in the context of

Conference Review System (CRS). Also, an author is

prohibited to submit a paper after submission

deadline.

(deftemplate AuthorPaperSubmission

(slot agentid)(slot group)(slot role)

(slot status (type STRING)))

(deftemplate SubmissionProhibition

(slot agentid)(slot group)(slot role)

(slot status (type STRING)))

(deftemplate rdPS

(slot submissionDeadline (type LONG)))

(defglobal ?*currentdate* = (System.

currentTimeMillis))

; check if currentDate > SubmissionDeadline

(defrule AuthorProhibitionRule

(AuthorPaperSubmission

(agentid ?author)(group ?gr)

(role ?role)(status ?s))

(rdPS (submissionDeadline ?sdl))

(test (> ?*currentdate* ?sdl))

(test (= ?s "preregistred"))

(assert (SubmissionProhibition

(agentid ?author) (group ?gr)

(role ?role) (status "LeavingSystem")))

(printout t "Author " ?author " is

Prohibited to submit a paper " crlf))

Snippet 2: Prohibition norm for author paper submission.

Norm modification process consists of dynamically

modifying norms’ temporal constraints in order to

give more time to agents to adapt their behaviours

with normative ones. Hence, norm modification will

take place mainly after checking the non-achievement

of the expected system state. JESS provides support

for updating norm related facts by using constructs

like: defquery, modify, assert.

In norm enforcement process, norm monitoring

module observes, permanently, each change made on

instantiatedNormList and check which norm is

currently in force. In the case of obligation norms,

norms monitoring tracks if current performed action

by a given agent is submitted to an obligation and

check if the obligation deadline is reached. Norm

instantiator agent asserts, consequently, the

obligation related fact in the Working Memory

(WM). This means that action performed by a given

agent has been submitted to an obligation. Also,

asserted fact encompasses a set of agents’ relevant

information such as: agentID, agentRole,

agentGroup, and currentAgentStatus. In order to

determine fulfilled or violated obligations, norm

monitoring gets, after deadline expiration, asserted

obligation facts from the WM. By formulating a set

of JESS queries to the WM. An obligation is

considered as fulfilled, if current agent status matches

with status indicated in the obligation. Conversely,

obligation is considered as violated. A particular list

named: normStatusList is maintained by norm

manager for updating obligation enforcement-related

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information.As far as normStatusList is dynamically

modified, norm manager informs norm enforcer to

proceed to punish or reward agent in question.

Punishments and rewards are domain dependent. In

the context of NC4OMAS, multiple lists are

maintained for managing rewarded and punished

agents (i.e., rewardedAgentList and

punishedAgentList respectively). Also, putting

rewarded or punished agents in such list is the

simplest way to behave with. Agent manager informs,

at runtime, sanctioned agents by the result of

enforcement process. In the case of prohibition, an

agent will be rewarded if its status does not much with

one indicated in the norm.

5 DISCUSSION

NC4OMAS is proposed mainly in order to control

AGR-based OMAS using a norms-based control

process. JESS is used to deal with norm specification

and modification issues thanks to its flexibility in

which JESS can be tightly coupled to JAVA code.

AspectJ is used, basically, for monitoring purposes.

However, AspectJ coding needs a particular attention

when manipulating data structures like, HashMap,

ArrayList, etc. These data structures are accessed by

both aspects for putting or updating data and

NC4OMAS agents for getting specific information

like activatedRequestedRoleList, instantiatedNorm

List, desactivatedNormList, etc. Therefore, data

structures with multiple access need a particular

control in order to avoid JAVA exceptions like

ConcurrentModificationException. To avoid such

exception, ConcurrentHashMap is opted instead

HashMap and CopyOnWriteArrayList instead

ArrayList and so on.

In order to address limitations and advantages of

our approach, Table 1 summaries the most relevant

works on norms-based control according to some

comparison criteria we proposed. Norm enforcement

(i.e., rewards and penalties). Norm modifications

means the ability of dynamically updating some norm

settings (i.e., temporal constraints). Distributed

enforcement architecture consists of the way for

doing enforcement mechanism (i.e., centralised or

distributed). Performed actions mean the possibility

of monitoring actions performed by agents. Norm

lifecycle investigates the evolutionary process of

norm’s lifecycle developed over several phases:

creation, instantiation, emergence, adoption,

internalization, and norm removal. Exchanged

messages underline the way in which agents

implicated in control process communicate with.

Norm representation indicates which formalism is

used to represent norm either rule-based systems,

deontic logic, binary strings or game theory. As a

MAS is constituted of environments, organizations,

Table 1: Summary of norms-based control proposals.

Criteria

Proposals

Norm

Enforcement

Norm Modification

Distributed enforcement

architecture

Performed actions

Norm lifecycle

Exchanged messages

Norm Representation

Deontic Concepts Context

Conflict resolution

Punishment

Reward

Permission

Obligation

Interdiction

Recommendation

Interaction

Environment

Organization

(Felicíssimo et al., 2008)

√ √ √ √

(Garcia-Camino et al.,

2005)

√ √ √ √ √ √ √ √

(Ahmad et al., 2016)

√ √ √ √ √ √ √ √ √ √ √

(Dastani & van der Torre,

2004)

√ √ √ √ √ √ √ √ √ √ √

(Criado et al., 2013)

√ √ √ √ √ √ √ √ √ √ √ √

(Mahmoud et al., 2014)

√ √ √ √

(Alechina et al., 2018)

√ √ √ √ √

(Marir et al., 2019)

√ √ √ √ √ √ √ √

NC4OMAS √ √ √ √ √ √ √ √ √ √ √ √ √

NC4OMAS: A Norms-based Approach for Open Multi-Agent Systems Controllability

169

and agents interacting and playing roles, context

criterion is chosen for specifying which issue is

addressed by proposed normative approaches.

Finally, conflict resolution denotes the fact that

proposals are endowed with capabilities for applying

conflicts resolution techniques between norms. A

normative conflict arises when a given agent is

prohibited and obliged to perform the same action at

the same time (Belchior et al., 2018).

In light of these comparison results described in

Table 1, NC4OMAS takes a remarkable place

between existing proposals. Our proposal joins the

majority of proposed approaches in the enforcement

process, performed actions, norm representation,

deontic concepts in terms of permission, obligation,

and prohibition. Also, our work joins (Criado et al.,

2013) and (Alechina et al., 2018) in the distributed

enforcement architecture criteria in which we adopt

AGR model for implementing OMAS. Also, AGR

model allows decomposition over groups of roles.

With regards to norm lifecycle, our approach joins

proposals of (Criado et al., 2013) (Mahmoud et al.,

2014) and (Dastani & van der Torre, 2004) in which

norms are submitted to several phases starting with

creation, instantiation, enforcement, and finally

removal. In contrast, norm conflict resolution and

normative environment are excluded in NC4OMAS.

In our approach, there is no need to impose any

constraints (i.e., norms) on agent entrance and/or

departure and required capabilities for an agent for

doing a requested role. The purpose of norm

modification allows a dynamic update of norm

settings expressed in terms of temporal constraints.

This latter makes the behaviour specified in the norm

more flexible and gives, as a result, an opportunity for

agents to adapt their behaviour with normative one.

6 CONCLUSION AND FUTURE

WORKS

In this paper, a Norms-based Controllability approach

for Open Multi-Agent systems (NC4OMAS) was

proposed. The idea of NC4OMAS consists of

delegating a third-party MAS in order to manage

norm related issues (i.e., norm instantiation, norm

monitoring, and norm enforcement). Delegated MAS

is designed in a distributed way in which agents

implied in control process are dynamically dispatched

over system groups. The originality of our

proposition is the runtime control by considering its

current system state and the target one using AspectJ

for norm monitoring compliance and JESS for

specifying, updating and making inference over

norms. Currently, a software tool called NC4OMAS

tool is being developed in order to demonstrate the

feasibility of our approach. NC4OMAS tool is

designed as a middleware and will be executed

concurrently with the system submitted to control in

order to maximize compatibility with any type of

AGR-based platform.

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