Cohesion as a Tool for Maintaining the Functional Integrity of a

Multi-agent System

Mickael Bettinelli

, Damien Genthial

and Michel Occello

Institute of Engineering Univ. Grenoble Alpes, Grenoble INP, LCIS, 26000 Valence, France

Keywords:

Multiagent System, Functional Integrity, Cohesion.

Abstract:

In a context of open systems, agents can work with other unknown agents. They must therefore be able to

dynamically adapt their behavior to ensure that the system functions properly for all times. Bringing together

these open groups of agents and humanities and social sciences groups opens up new perspectives in how to

maintain the functional integrity of an artiﬁcial system. We propose then to draw inspiration from mechanisms

of cohesion resulting from HSS in order to improve the resilience of these systems.

1 INTRODUCTION

Today, a large majority of digital systems are highly

distributed and open involving heterogeneous entities

that interact and have advanced decision-making ca-

pabilities. Considering these artiﬁcial systems as so-

cieties of intelligent systems or objects opens up wide

perspectives through the analogy we can make with

social organizations. These systems are composed of

groups of agents who may need to work as a team

to achieve a common objective. These groups need

cohesion to maintain their agents united and achieve

their objectives. Cohesion is a concept that refers to

the mechanisms that connect a small or large set of

units. In computer science, the notion of cohesion

has so far been mainly limited to structural aspects

of group organization resulting in the maintenance

of connectivity in relationships between individuals

(swarm of robots (Manning et al., 2015), software

components (Rathee and Chhabra, 2018), networks

(Torrents and Ferraro, 2015)). Maintaining functional

integrity in a decentralized artiﬁcial system represents

the ability of a multi-agent system to achieve its ob-

jective (Pie¸tak et al., 2009). Different factors such

as the number of agents in the system or the num-

ber of inter-agent communications can make a system

inefﬁcient or even fail, and therefore threaten its func-

tional integrity (Kisiel-Dorohinicki and Nawarecki,

1998),(Wallach, 1981).

https://orcid.org/0000-0002-0636-8311

https://orcid.org/0000-0002-1200-8371

https://orcid.org/0000-0001-8727-8010

To avoid failure, a multi-agent system must be

able to self-organize in order to maintain the unity of

its members and thus restore its performance. Main-

taining the integrity of these artiﬁcial societies is then

close to maintaining cohesion (Carron, 1982) in hu-

man societies and we can beneﬁt from studies con-

ducted in the Humanities and Social Sciences (HSS).

The notion has been discussed very recently for mod-

eling in behavioural simulations within an multi-

agent system (MAS) (Adam et al., 2019). While

much work is being done on fault tolerance, few

use behavioural concepts related to group dynam-

ics. Some studies address the self-evaluation of an

agent in relation to members of its group through

the notion of social diagnosis (Kalech and Kaminka,

2003)(Rooy et al., 2016). They only approach the no-

tion of cohesion from the point of view of its individ-

ual evaluation. In this article, we propose an agent

model based on the cohesion mechanisms of HSS in

order to improve the functional integrity of MAS.

This document is organized as follows: part 2 in-

troduces the notion of group cohesion as seen in HSS

as well as a list of cohesion criteria that can be in-

tegrated into artiﬁcial systems to improve their re-

silience. Section 3 presents a case study and describes

how to integrate cohesion criteria into it. Section 4 de-

scribes the cohesive agent model we propose. Section

5 presents the experimental environment and speci-

ﬁes the methods of evaluating the system on the study

case and the associated results. Finally, we conclude

in the part 6.

Bettinelli, M., Genthial, D. and Occello, M.

Cohesion as a Tool for Maintaining the Functional Integrity of a Multi-agent System.

DOI: 10.5220/0008920404450452

In Proceedings of the 12th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2020) - Volume 2, pages 445-452

ISBN: 978-989-758-395-7; ISSN: 2184-433X

445

2 COHESION

2.1 Group Cohesion

2.1.1 Deﬁnition

Cohesion is deﬁned by Festinger as the sum of the

forces that act on members in order to maintain the

group (Festinger, 1950). These forces depend on the

attraction and repulsion of several criteria such as the

prestige of the group, its members, or the tasks the

group is working on. Later, (Carron, 1982) adds the

notion of unity and describes cohesion as a dynamic

process that reﬂects the tendency of group members

to stay together and maintain unity in the pursuit of

common goals.

2.1.2 Cohesion Measurement

Mikalachki suggests that cohesion can be divided into

two components (Mikalachki, 1969) : task cohesion

and social cohesion. He argues that task cohesion oc-

curs when group members come together around the

task they are supposed to perform, while social cohe-

sion occurs when they come together around a social

function. Several models have been created to break

down group cohesion.

The ﬁrst presents the two main categories sug-

gested by Mikalachki, task cohesion and social co-

hesion. The second ﬁgure 1, presented by (Carron,

1985), takes into account the individual’s attraction to

the group as well as its integration into the group. It

presents two dimensions of cohesion: the Group In-

tegration (GI) and the Individual Attraction To Group

(ATG). The ﬁrst (GI) represents the way individuals

perceive the group (similarity, proximity, etc.), the

second (ATG) represents the satisfaction of the ex-

pectations that the group brings to individuals (inter-

actions with others, productivity, objectives, etc.). As

shown in ﬁgure 1, the model decomposes again these

two dimensions as Mikalachki did in order to split

them into social and task components. Therefore,

Carron proposes a multi-dimensional model where

the cohesion criteria are divided into the dimensions

GI-S, GI-T, ATG-S and ATG-T.

Heuzé and Fontayne tried to deﬁne a French-

speaking measure of cohesion (Heuzé and Fontayne,

2002). To this end, they examined the useful-

ness of the Group Environment Questionnaire (GEQ)

(Carron, 1985) for measuring cohesion in French

sports teams and the reliability of the Carron model.

Their study presents a questionnaire similar to the

GEQ called Questionnaire sur l’Ambiance du Groupe

(QAG). Thus, about thirty criteria were rewritten on

Figure 1: Proposed model by Carron (1958).

the QAG, and distributed over the four dimensions

seen above: GI-S, GI-T, ATG-S, ATG-T. Each of

them asserts a feeling about the group that the inter-

viewee may or may not agree with, for example, I

don’t like my team’s playing style, or I have some of

my best friends on the team.

In addition to the QAG items, other factors are

favourable to the creation of a team climate. Unlike

the QAG, they rather focus on the state of the group

itself than on the perceptions of individuals. Carless

and De Paola are conducting a study similar to Car-

ron’s regarding the cohesion measurement in which

they focus on work teams (Carless and De Paola,

2000). By seeking correlations between dimensions

of cohesion and characteristics of working groups,

they determine new criteria for assessing group co-

hesion. These are strongly correlated with the task

cohesion: team interactions, team effectiveness, pres-

ence of social assistance, presence of team spirit.

2.2 Artiﬁcial Cohesion

In order to transpose the cohesion seen in HSS to ar-

tiﬁcial systems, it is necessary to extract quantiﬁable

criteria from sociology to be able to reuse them in a

decision model based on cohesion. We extract crite-

ria from the QAG cohesion and add those of Carless

and Paola (Carless and De Paola, 2000) (table 1), we

retain a list of 18 items allowing the appearance of

cohesion within a group. To give some examples of

extracted criteria : Satisfaction of the team’s objective

(ATG-T), Presence of afﬁnities in the group (ATG-S),

Cooperation in the team (GI-T), Interactions in the

team (GI-S), etc.. All these items represent a part of

the QAG and assess both the social and task dimen-

sions of the group. Each individual then builds a per-

ception of its group that allows him to have or not a

feeling of belonging to it. Although the QAG contains

31 items, this list contains only 18. The main reason

is that many of the QAG criteria are too social to be

integrated into an artiﬁcial system. To illustrate : I

don’t like to participate in my team’s extra-sport ac-

ICAART 2020 - 12th International Conference on Agents and Artiﬁcial Intelligence

446

Figure 2: Mars exploration with agents.

tivities assumes that an agent can like or dislike a task

and that its group can organizes extra-group activities.

This kind of criterion requires a very cognitive agents’

architecture in order to be used.

3 INTEGRATE CRITERIA INTO

AN ARTIFICIAL SYSTEM

3.1 Case Study

Cohesion mechanisms must be evaluated on a case

study in which coalitions of agents are formed. The

chosen case study is about Mars exploration, pre-

sented in Ferber’s book (Ferber, 1995) (ﬁgure 2). The

objective is to collect mineral samples around a base

on Mars using robots, each representing an agent. In

Ferber’s example, there are three types of agents: 1.

detectors (which explore the planet in search of min-

erals), 2. drillers (which extract the ore from the

ground), 3. carriers (which bring the ore back to the

base).

By their nature, agents are interdependent, carri-

ers cannot transport ore if drillers have not extracted

it, and drillers cannot extract it if detectors have not

found it. Each agent is constrained in its communi-

cations by a maximum emission range. In this prac-

tical case, a team of valid agents is composed of at

least three members, one of each available type (de-

tector, driller, transporter). This case study therefore

requires agents to cooperate to achieve their personal

goals. They must then organize themselves into coali-

tions to be able to help each other.

3.2 Criteria Integration Into this Case

Study

Provided advantages by cohesion criteria :

• self-organization assistance: agents tend to self-

organize into coalition;

• increased productivity: the system ﬁnishes its

work faster by communicating less;

• increased resilience: the system better manages

stress periods without failing;

• failure detection: the system better detects block-

ing malfunctions during the execution period.

The Table 1 illustrates the beneﬁts provided by each

cohesion criterion. Ease of implementation is taken

into account to help system designers to choose which

criteria to integrate ﬁrst. Increasing resilience is not

really useful in this table since it is the more or less

direct consequence of each cohesion criterion.

Relatively few criteria are easily integrated into ar-

tiﬁcial systems. Indeed, most of them require a rela-

tively complex cognitive agent structure in order to

be implemented. For example, social assistance and

the cooperation of agents requires having a memory

on which they can reason to determine who should

be helped, how, when, etc., but also speciﬁc commu-

nication protocols to act effectively. Similarly, team

spirit is a complex element to put in place since it re-

quires agents to plan actions according to their inter-

nal states. On the contrary, some criteria are more eas-

ily integrated, such as the satisfaction of the agent’s

involvement in its individual objective, or the quality

of relationships through the agents’ interactions.

In order to evaluate cohesion criteria into an arti-

ﬁcial system, we give priority to those that are eas-

ily implemented and cover a wide range of beneﬁts

among those presented in the table 1 (allowing self-

organisation, increasing productivity and resilience,

and detecting failures). Finally, we retain all the crite-

ria that can be easily implemented: 1. satisfaction of

the member’s involvement in the objective, 2. interac-

tions in the team, 3. presence of afﬁnities in the group,

4. satisfaction of the role played in the group, 5. sat-

isfaction of the role acquired in the group. We have

chosen to remove criteria #4 and #5 because both fo-

cus on the role that agents play and in our case study

each agent have a ﬁx role. However, if the case study

uses multipurpose agents, criteria #4 and #5 would

have a more important effect and could play a role in

self-organization.

Finally, the cohesiveness criteria integrated into

our case study are: satisfaction of the member’s in-

volvement in the objective, interactions in the team as

well as presence of afﬁnities in the group.

4 COHESIVE AGENT MODEL

In order to generalize the use of cohesion criteria, we

propose a cohesive agent model, which, based on the

Cohesion as a Tool for Maintaining the Functional Integrity of a Multi-agent System

447

Criteria

Increased resilience

Ease of implementation

Increased productivity

Self-organization assistance

Fault detection

Satisfaction of the member’s involvement in the

objective

X X X X

Interactions in the team X X X

Presence of team spirit X X

Effectiveness of the team X X X

Presence of afﬁnities in the group X X

Satisfaction of the role played in the group X X X

Satisfaction with how the team performs its goal X X X

Satisfaction of the team’s objective X X

Ability of the individual to evolve within the

group

X X X

Importance of the social group to the individual X

Satisfaction with the role acquired in the group X X X

Cooperation in the team X X

Involvement of group members in the activity X X X

Appreciated atmosphere within the group X X

Presence of social assistance X X

Satisfaction of team priorities X X X

Preference for activities of other groups X X X

Compatibility of individual objectives with the

common objective

X X X

Table 1: Advantages provided to artiﬁcial systems by cohesion criteria.

above criteria, requires the following characteristics:

• a representation of the environment;

• a representation of itself;

• social skills;

• the ability to reason on its knowledge.

These properties can be found in several known cog-

nitive architectures such as Soar (Laird, 2012), ACT-

R (Anderson et al., 1997), CLARION (Sun et al.,

1998), LIDA (Franklin and Patterson, 2006), BDI

(Bratman, 1991), or FORR (Epstein, 1994).

These six architectures meet our needs for rep-

resenting the environment, itself and reasoning on

knowledge. Some, such as Soar, BDI, ACT-R and

CLARION, even make it possible to create social

agents. Many of these architectures (Soar, LIDA,

FORR) also have learning capabilities that are beyond

the scope of this study. ACT-R and CLARION archi-

tectures are very time-consuming to compute, which

makes them difﬁcult to use on a large number of

agents and would avoid scaling. A simple architec-

ture based on the BDI model therefore seems to be the

most representative of our needs among those stud-

ied. For this reason, we reuse and modify it in order

to experiment with the effect of cohesion criteria in

artiﬁcial systems.

The changes to the classic BDI model ensure that

the agent can take into account the states of its ac-

quaintances to sort incoming messages as well as its

personal state to make a decision. Thus, the cohesive

BDI model (ﬁgure 3) is modeled as the tuple:

cohesi f

= < Messages, S

, ﬁlter, Acquaintances,

Implication, Desires , plans, ctx, Intentions, S

This model uses the same reasoning cycle as a

classic BDI model with some modiﬁcations (high-

lighted on the ﬁgure 3).

Message Management (Messages, S

). The Mes-

sages module is a buffer in which all messages re-

ceived by the agent are stored. These messages are

stored before being processed by the agent. The mes-

sage selection function allows to choose which mes-

sage will be processed by the agent. In this case,

the messages are processed in their order of receipt

(FIFO) but it is possible to design a heuristic to adapt

ICAART 2020 - 12th International Conference on Agents and Artiﬁcial Intelligence

448

Figure 3: Reasoning cycle of the suggested modiﬁed BDI

model.

the agent’s behavior to the needs.

The Belief Update Function. The belief update

function provides the agent with new information

built by its perceptions or by messages received from

other agents. The updating of beliefs impacts the

plans module, which will allow the agent’s behaviour

to be modiﬁed according to his knowledge.

Implication, Acquaintances. The agent’s belief sta-

tus is enhanced by the addition of involvement and

bridging modules. The ﬁrst is a self-assessment by

the agent of the quality of its involvement in its work.

Involvement allows cohesive agents to notice a lack

of activity and react accordingly via the ctx module.

This ﬁrst mechanism provides agents with a means of

self-diagnosis of a dysfunction, which improves their

individual resilience and the system resilience.

The Acquaintances module associates a relational

score with each acquaintance, which makes it possi-

ble to estimate the quality of each relationship. Since

the Acquaintances module has an effect on the mes-

sage ﬁlter function, this score is an analogy of trust.

By quantifying the reliability of an agent’s acquain-

tance, this second mechanism allows an agent to de-

tect dysfunctional agents with which it is in contact.

The detection of these agents then allows the system

to isolate them and thus improve its resilience.

The Filter Function. The ﬁlter function is used to

ﬁlter messages in relation to the agent’s connection

module. Relational scores of acquaintances are com-

pared to a threshold used to determine whether a re-

lationship is bad enough or not in order to ignore the

sender’s messages. Therefore the ﬁlter function plays

an important role in the recognition of dysfunctional

agents and therefore in maintaining the functional in-

tegrity of the system agents.

Desires, Intentions. Desires are the agent’s motiva-

tions. They represent objectives or situations that the

agent would like to see accomplished. Intentions rep-

resent what the agent wants to do. Unlike desires,

agents check the feasibility of intentions. Intentions

are achievable, some of which can already be started.

Figure 4: Activity diagram of a cohesive agent.

Plan Generation (Plans, ctx). In order to act, agents

generate several plans linked to their desires. The

context function prioritizes plans according to the sit-

uation by assessing their usefulness and selects only

those that are applicable. At the end of this step, the

agent has an overview of the achievable plans and

their usefulness.

Intentions Management (S

, Execute Intentions).

At this level, all feasible plans are selected. The se-

lection of the intention will therefore make it possible

to keep only one plan and choose an action to exe-

cute. The selected plan is the one with the highest

utility score.

To illustrate the behaviour of one of the three types

of agents, the ﬁgure 4 shows the activity diagram of

a drilling agent. This diagram represents the mech-

anisms for involvement and evaluation of acquain-

tances explained above. The ﬁrst cohesion mecha-

nism is involved in the selection of the message to

socially isolate agents who do not do their work prop-

erly. The second, on the choice of a plan, allows the

agent to change its behaviour in case of dissatisfaction

with the work previously done. These two comple-

mentary mechanisms both contribute to improving the

resilience of the system by making it possible to self-

diagnose a malfunction or by isolating agents from

the system that have a failure. We will show in the

section 5 the effect of these mechanisms on the case

study.

Cohesion as a Tool for Maintaining the Functional Integrity of a Multi-agent System

449

5 SYSTEM EVALUATION

5.1 Experimentation

The experiment is built on the MASH software (Ja-

mont and Occello, 2015). MASH (Multiagent Soft-

ware / Hardware simulator) is a tool for simulating

and executing multi-agent embedded systems. Agents

are implemented in Java and are executed by this sim-

ulator. We use MASH to reproduce the case study and

test different solutions built from conﬁguration ﬁles,

themselves generated pseudo-randomly using Python

scripts. Agents work with the cohesive BDI model

explained in the previous section. As seen, the rea-

soning cycles between the classical BDI model as we

introduced it and the cohesive BDI model are very

similar. This similarity allows us to compare the ef-

fectiveness of the two models and see what beneﬁts

cohesive agents can bring to the system.

In the following sections, we will assess the re-

silience of the cohesive system to see if the cohesion

mechanisms have resulted in an improvement in its

resilience. We will also compare the performance of

these systems to see if the addition of these mecha-

nisms changes their effectiveness.

5.2 Insertion of Faults

Failures are inserted in some agents of our simulated

systems. The objective is to see the effects of these

dysfunctions on these systems composed either of co-

hesive agents (implementing cohesion mechanisms)

or of conventional agents (without cohesion mecha-

nisms). The effect of these failures on each system is

then compared to see the beneﬁts that cohesive agent

mechanisms can provide.

5.2.1 GPS Malfunction

In this scenario, agents continue to behave normally

and try to get the ore drilled by the drilling agents

to the base. Carriers have a faulty GPS that prevents

them from time to time from moving to the desired

position. The self-assessment mechanism of their in-

volvement is then useful to correct their behaviour.

The ﬁgure 5 compares the duration of the exper-

iment (in seconds) between a system composed of

classical agents (classical system) and a system com-

posed of cohesive agents (cohesive system) with dif-

ferent numbers of ores. Naturally, the more minerals

there are in the environment, the longer it takes for

agents to pick them up. Similarly, it is natural that

the two types of agents in the systems evaluated are

Figure 5: Duration of the experience depending on the type

of agent and the number of ores in the environment.

not quite as fast as each other. It is therefore inter-

esting to note that the gap between the two curves

is not constant but is gradually widening. This dif-

ference is explained by the difference in reactivity of

cohesive agents compared to conventional agents. In-

deed, as explained in the previous sections, cohesive

agents integrate an involvement mechanism that al-

lows them to self-assess their activity. Thus, when a

transport agent goes to the wrong coordinates, it is un-

able to recover the ore from the driller who contacted

it. Unable to fulﬁl its role, the agent’s involvement

decreases to a certain level which leads it to search a

new task. A conventional transport agent without this

type of mechanism goes to the wrong coordinates and

waits a long time for the driller to give it the ore. Fi-

nally, the transport agent is unblocked through a reset

mechanism that allows the blocked agents to return to

the base to continue their work. In short, in this sce-

nario the cohesive agent system is more reactive to er-

rors, agents recover more quickly and dynamically by

estimating their involvement in their activity through

a ratio between inactivity time and working time. On

the contrary, the conventional agent system is not very

reactive to failures, which can be seen by a more rapid

evolution of the duration of the experiments.

5.2.2 Propagation of False Information

In this scenario, the detector is no longer able to dis-

tinguish between empty and solid ores. When it de-

tects the position of an ore, it moves towards it and

saves it’s position. Then, it informs the driller of its

position to recover it. A properly functioning detector

updates its knowledge of minerals around itself. On

the contrary, a defective detector agent is no longer

able to distinguish empty ores from others. For ex-

ample, it is asking for help from drillers to extract

ore from sites where there is no ore left. Therefore,

ICAART 2020 - 12th International Conference on Agents and Artiﬁcial Intelligence

450

the traditional system cannot maintain its functional

integrity since the defective agent constantly calls its

acquaintances who try to help without ever question-

ing its requests. In short, the system falls into an end-

less loop and is unable to detect the end of the work.

In this case, the failure is large enough to completely

block the system so it is considered as a failure. The

message selection mechanism then comes into play

by allowing cohesive agents to maintain the system’s

operation. Indeed, in the case of a system composed

of cohesive agents, the agents isolate the defective in-

dividual thanks to a conﬁdence score held for each of

their acquaintance. When an agent is defective and its

behaviour is counterproductive to its peers, its rela-

tionships decrease its trust score. At a certain thresh-

old, agents stop taking into account messages that the

defective agent send. The cohesive system can there-

fore correctly estimate the progress of the work and

maintain its functional integrity. It is then more re-

silient than the traditional system to failures of this

type.

In this scenario, unlike the previous one, the node

of the distributed system does not isolate itself, it is

excluded by the system because of its deviant behav-

ior. Generalizing, this type of mechanism would be

useful in the case of an attack on the system. An

externally controlled agent attempting to change the

expected behaviour of the system would be automat-

ically removed by the system, ensuring that the func-

tional integrity of the system is maintained.

5.3 Comparison of System Efﬁciency

In order to compare the efﬁciency of systems, we

compare the rate of ore recovery, the duration of ex-

periments and the communications load. The ﬁgure

6 shows that cohesive agents have a very similar ore

collection rate to conventional agents. While the min-

eral collection curve of conventional agents marks 2

large steps (both minerals are collected very quickly

when found), cohesive agents tend to collect them in

several stages. This is due to the conﬁguration of

cohesive agents that tend to change activity quickly

when they are not working (when drillers are wait-

ing for transporters, for example). Agents who have

been inactive for too long return to the base and lose

time in achieving the overall objective. However, the

graph also shows that the execution of experiments

with cohesive agents seems shorter than with con-

ventional agents. Indeed, experiments composed of

cohesive agents last an average of 67 seconds com-

pared to 80 seconds with conventional agents. Cohe-

sive agents reorganize more quickly when detectors

no longer ﬁnd minerals to drill and agents return to

Figure 6: Ores reported as a function of time by agent type

with an implication threshold of 0.6 for cohesive agents.

the base more quickly than in systems composed of

conventional agents.

In the same way as the rate of ore recovery, the

number of messages transmitted in the system is al-

most identical for both types of agents. However, co-

hesive agents send 2.8% more messages than classi-

cal agents, which may be explained by the fact that

they take a little longer to collect the ore while having

shorter overall experience execution times.

Finally, these experiments show that the cohesion

mechanism does not improve the efﬁciency of the sys-

tem, but that the cohesive system shows better re-

silience and failure resistance than the conventional

system in the scenarios studied, without introducing

any signiﬁcant additional cost into its operation.

6 CONCLUSION

We presented how group cohesion mechanisms can

guide a new approach to agent design in order to

maintain the functional integrity of a MAS. These

mechanisms were evaluated using a case study and

compared to more conventional designed agents. As

a ﬁrst conclusion, we have seen that the integration

of cohesion mechanisms into artiﬁcial systems has in-

creased their resilience. We have not noted any degra-

dation in the efﬁciency of the system in mineral col-

lection or in the number of communications. In ad-

dition, the reactivity of the cohesive system is better

than the reactivity of the conventional system, which

reduces the average time of the experiments. In con-

clusion, cohesive agents have the ability to maintain

the functional integrity of their system while limiting

the negative impact of their behaviour compared to

conventional agents.

Although increasing resilience is a relevant ben-

eﬁt, few cohesion criteria have been integrated and

Cohesion as a Tool for Maintaining the Functional Integrity of a Multi-agent System

451

evaluated here. For future research, it would be inter-

esting to try to integrate new cohesion criteria in dif-

ferent and more advanced cases to see, for example,

whether cohesion mechanisms can improve produc-

tivity or whether they can generate (or improve) the

self-organization of a system of multipurpose agents.

This model is currently being integrated into a de-

cision support system for the Circular project. This

project focuses on developing the necessary technolo-

gies and conditions to make new circular industrial

systems able to transform post-used products into new

products. Post-used components are avatarized as

agents. These cohesion criteria can be integrated into

the Soar architecture and used by the agents to form

groups that represent the products to make.

ACKNOWLEDGEMENTS

This research is supported by the French National Re-

search Agency under the "Investissements d’avenir”

program (ANR-15-IDEX-02) through the Cross Dis-

ciplinary Program CIRCULAR.

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