A Self-adaptive Module for Cross-understanding in Heterogeneous

MultiAgent Systems

Guilhem Marcillaud

, Val

erie Camps

, St

ephanie Combettes

, Marie-Pierre Gleizes

and Elsy Kaddoum

Institut de Recherche en Informatique de Toulouse, Universit

e Toulouse III Paul Sabatier, Toulouse, France

Institut de Recherche en Informatique de Toulouse, Universit

e Toulouse II Jean Jaures, Toulouse, France

Keywords:

Cross-understanding, Data Imputation, Multi-referential Information, MultiAgent System, Heterogeneous

Agents.

Abstract:

We propose a self-adaptive module, called LUDA (Learning Usefulness of DAta) to tackle the problem of

cross-understanding in heterogeneous multiagent systems. In this work heterogeneity concerns the agents us-

age of information available under different reference frames. Our goal is to enable an agent to understand

other agents information. To do this, we have built the LUDA module analysing redundant information to

improve their accuracy. The closest domains addressing this problem are feature selection and data imputa-

tion. Our module is based on the relevant characteristics of these two domains, such as selecting a subset of

relevant information and estimating the missing data value. Experiments are conducted using a large variety

of synthetic datasets and a smart city real dataset to show the feasibility in a real scenario. The results show an

accurate transformation of other information, an improvement of the information use and relevant computation

time for agents decision making.

1 INTRODUCTION

This paper tackles the problem of cross-

understanding in a heterogeneous MultiAgent

System (MAS) (Wooldridge, 2009). In such a system

each agent has a local view of its environment and

its own understanding of this view i.e. its reference

frame (Hoc and Carlier, 2002). To enable an agent

to use its perception, the agent decision-making

process is adapted to its reference frame. In this

work, we deﬁne two agents as heterogeneous when

their reference frame is different. As information

communicated by an agent is shared according to its

reference frame, different agents will have trouble to

understand each other. In systems where cooperation

is needed, understanding problems may lead to

non-cooperative situations (Georg

e et al., 2011).

The cross-understanding problem can be encoun-

tered in several problems. For example, let consider

the Internal Decision Layer of an Autonomous Vehi-

cle (Loke, 2019). It is designed to take a decision with

information from a set of sensors perceiving informa-

tion in a reference frame (speed 30 km/h). To enrich

its world understanding the vehicle may have access

to other vehicles information through communication

(speed 18.64 mph). However, a reference frame dif-

ference is an obstacle to the correct use of this infor-

mation.

The studied problem possesses characteristics

from the Feature Selection (Gibbons et al., 1979) and

Data Imputation (Efron, 1994) ﬁelds. The Feature Se-

lection one consists in learning the feature from the

set of communicated information that is related to the

missing feature (Li et al., 2017). The Data Imputation

ﬁeld focuses on estimating missing data from past ob-

servations (Van Buuren, 2018).

This paper addresses the problem of enriching the

understanding of an agent in a MAS using informa-

tion from neighbour agents having their own refer-

ence frame. This can be decomposed into two sub-

problems: 1) enable an agent to understand commu-

nication from heterogeneous agents and 2) improve

the information value using multiple sources (Zhao

and Liu, 2008).

For the ﬁrst sub-problem, the module we have

developed, called LUDA (Learning Usefulness of

DAta), supposes the existence of a linear relationship

(Montgomery et al., 2012) between two data repre-

senting the same information in different reference

frames. As LUDA aims at being added to an already

Marcillaud, G., Camps, V., Combettes, S., Gleizes, M. and Kaddoum, E.

A Self-adaptive Module for Cross-understanding in Heterogeneous MultiAgent Systems.

DOI: 10.5220/0010298503530360

In Proceedings of the 13th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2021) - Volume 1, pages 353-360

ISBN: 978-989-758-484-8

353

functioning agent decision-making process the com-

putation impact must be limited. Thus, we strongly

emphasise the requirement for a limited number of

histories necessary to learn reference frame transfor-

mation.

Once the agent is able to transform different ref-

erence frames, it has access to multiple sources of

the same information and focuses on the second sub-

problem. This information redundancy can be used

to improve the accuracy in a noisy environment (Hall

and Llinas, 1997). To enable that, we extended LUDA

with a novel agent behaviour that combines multiple

information instances of the same information.

This paper is organised as follows: in section

2 that starts with an overview of Feature Selection

and Data Imputation, we propose a description of the

cross-understanding problem presented in this paper.

From this description, section 3 describes the LUDA

system. Section 4 discusses several experiments as-

sessing the effectiveness of LUDA. Finally, section 5

concludes the paper and presents some perspectives.

2 STATE OF THE ART AND

PROBLEM FORMALISATION

Cross-Understanding in MAS is a problem that has

not been studied much even if the problem character-

istics are not new. From existing works, we have iden-

tiﬁed two domains, feature selection and data im-

putation, that possess similar characteristics with the

cross-understanding problem. Indeed, in the studied

problem, an agent has access to its own perceptions

and to those of its neighbours agents through com-

munications. Thus, the agent with missing percep-

tions can complete them from perceptions of neigh-

bour agents. This can be seen as a feature selection

problem. However, in our case, the heterogeneity

of agents reference frame makes the feature selec-

tion unsatisfactory because selected perceptions are

not always usable and an estimation of their relation-

ship with the known perception is needed. This is-

sue can be seen as a data imputation problem. Thus,

the cross-understanding problem in a heterogeneous

MAS consists for an agent to be able to select, from

its neighbouring agents perceptions, missing infor-

mation while translating them into its own reference

frame.

2.1 Feature Selection

The issue of variable and feature selection has been

extensively studied in recent years. This problem con-

sists in ﬁnding the most relevant variables to use (Gib-

bons et al., 1979) in different contexts, such as data

mining, to reduce the computation time (Duda et al.,

2006), biology to avoid a genetic study, medicine to

understand the cause of a disease (Niel and Sinoquet,

2018), simulation to select the best experiments con-

ditions (Ryzhov et al., 2012) or statistical studies re-

lated to economics.

In an intelligent and complex environment such

as a connected house or an intelligent vehicle, the ob-

servation of the environment comes from a multitude

of sensors. The noise and the redundancy of the per-

ceived data have to be taken into account. To avoid re-

dundancy as much as possible, the multi-source meth-

ods proposed by (Zhao and Liu, 2008) and Multi-view

methods (Wang et al., 2013) are used.

Difference with LUDA. More and more complex

feature selection problems are solved thanks to recent

advancements. One still remaining limitation con-

cerns the storage space needed to achieve accurate

results. Methods usually use a large amount of data

either received or continuously perceived (Li et al.,

2017). This can be problematic for some real case

problems when available computation time is limited.

As feature selection considers a problem with

multiple features for one result. Its objective is to re-

duce the features number to only keep the most rele-

vant ones without changing their value. As the deci-

sion process is a black box, the decision about which

data to choose follows the same issue as ours. Our

stated problem differs as it consists in replacing an

already known feature by another expressed in a dif-

ferent reference frame.

2.2 Data Imputation

Data imputation is the ﬁeld that focuses on estimat-

ing the value of missing data using multiple histories.

This ﬁeld addresses the issue of incorrect or missing

data (due to non-functioning or non-available sensors)

and improving data quality in noisy environments

(Marwala, 2009). Usually, data imputation methods

complete the existing dataset (containing enough ob-

servation) or estimate information in real-time from

the previous observation (Marwala, 2009).

Several methods are available for estimating miss-

ing data, which are classiﬁed into two categories:

mathematical (Van Buuren, 2018) and artiﬁcial intel-

ligence ones (Kumar et al., 2013). The most used

mathematical technique, the regression analysis (Liu

et al., 2018) enables to describe the relationships be-

tween information.

In the ﬁeld of artiﬁcial intelligence, neural net-

works are highly used (Heaton, 2008). Radial Basis

Function Network, a particular family of neural net-

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

354

works, is effective for regressions and functions ap-

proximation (Zhang and Suganthan, 2016) but has a

cost of computation and storage.

In data imputation ﬁeld, systems learn to esti-

mate new data values from previously observed data.

In contrast, in the cross-understanding problem, the

missing data is available but in a different reference

frame. Moreover, the correct data is lost among the

massive amount of available data. Using data imputa-

tion method for every available data is time consum-

ing and the cost of observation history would not be

adapted to real-time constraint applications.

2.3 Cross-understanding Formalisation

In the cross-understanding problem, an agent among

the agents set perceives from its environment several

information that we have classiﬁed into two subsets.

The ﬁrst subset called principal data (P

= {p

})

refers to information directly linked to the agent sen-

sors (i being one of the agent sensors) and which this

agent can understand. The second subset called ex-

tero data (E

= {e

}) refers to information com-

municated by its neighbouring agents ( j being one of

the agent neighbours), each having its own reference

frame. The cross-understanding problem in hetero-

geneous MAS consists for an agent in being able to

select from E

missing information of P

while trans-

lating them into its own reference frame.

The main objective of cross-understanding is to

enable an agent to use e

communicated by a neigh-

bour agent j to replace missing p

one for its deci-

sion process. The decision process uses a function

f and computes a result r to solve a situation. f is

considered as a black box taking a ﬁxed number of

principal data p

as inputs. The ﬁgure 1 illustrates an

example with 6 inputs, 3 p

, p

) and 3 e

When all principal data are available the computed

result is considered as the ideal one noted r

ideal

. When

the principal data are missing, the objective is to ﬁnd

the right extero data e

to replace them while min-

imising the difference between r

ideal

and r obtained

by f using e

For an agent, the problem can be formalised as

follow.

Given P

= {p

} and E

= {e

}

For Each p

∈ {P

1. select e

∈ {E

} that can be linked to p

2. determine the two coefﬁcients x and y such as

= x × e

+ y

(= p

)

(= e

)

(= p

)

(= e

)

(= p

)

(= e

)

Figure 1: The function f computes the result r using prin-

cipal data for i

and extero data for i

While Minimising:

r =

nbInput

∑

k=0

− e

| (1)

As different and several e

can be available for one

the LUDA’s second objective is to take advantage

of this multi-source to improve the accuracy of the

proposed values for p

, especially in a noisy environ-

ment.

3 THE ADAPTIVE MULTIAGENT

SYSTEM LUDA

This section presents LUDA a MultiAgent System

(MAS) enabling an agent to translate any data com-

municated into its reference frame.

3.1 MAS Overview

A MAS is composed of multiple interacting and au-

tonomous entities known as agents. An agent is

able to perceive a local part of its environment and

acts with this partial knowledge to autonomously

solve its own goals (Wooldridge, 2009). The MAS

paradigm is highly efﬁcient to solve complex prob-

lems thanks to the local and distributed computation

between agents (Serugendo et al., 2003).

An agent in LUDA respects the following proper-

ties: (i) it is autonomous, it decides alone its actions,

(ii) it interacts with neighbour agents to achieve its

goal, (iii) it has a partial view of the environment, (iv)

it has negotiating skills, and (v) it acts in a dynamic

and continuously evolving environment. In addition,

each agent possesses a characteristic named critical-

ity. Criticality is a numerical value that represents the

degree of non-satisfaction of the agent goals, which

impacts the agent behaviour.

A Self-adaptive Module for Cross-understanding in Heterogeneous MultiAgent Systems

355

3.2 LUDA Agents

We propose a MAS-based module to enable an agent

to understand communications from heterogeneous

agents. Based on the problem formalisation in section

2.3, we identiﬁed three types of agents: data agents,

morph agents and group agents as well as one en-

tity, the Decision Process (DP). The decision process

is a black box only able to compute a result with val-

ues given by agents (it corresponds to the f function

in ﬁg. 1).

Data Agents. For each information (belonging ei-

ther to E

or P

) a data agent is created and associ-

ated with it. The objective of each data agent is to

ﬁnd the other data agents representing the same in-

formation in a different reference frame. Let remind

that in this work, we assume that two different data

representing the same information are linked by a lin-

ear transformation: d

= x × d

+ y. To achieve this

objective, a data agent creates as many morph agents

as other data agents in the environment.

Morph Agents. Once created a morph agent is

associated with two data agents: its creator d

and

the objective one (d

) which is one of the data agents

of the environment. The morph agent goal is to ﬁnd

values of linear coefﬁcients that help its d

to approx-

imate the best linear transformation towards the value

of its d

. To modify the coefﬁcient values, the morph

agent uses an adaptation strategy exploiting the crit-

icality. The criticality of a morph agent is computed

from the error of the worst situation encountered dur-

ing its operation (i.e. a situation where the agent has

decided to accelerate instead of brake). From a sit-

uation, the morph agent stores as an observation the

value and the d

value. The observation is eval-

uated by comparing the d

value transformed by the

morph agent with the d

value acquired during this

observation (cf. equation 2). An observation is eval-

uated each time the coefﬁcients are modiﬁed. As the

scale of each reference frame is different, the differ-

ence value needs to be normalised using the principal

data range enabling the morph agents to interact with

each other.

crit(ma) = argmax(

|(x × d

+ y) − d

range(d

)

) (2)

Instead of using a linear regression, morph agents use

an adaptation process to compute coefﬁcients. This

process aims at reducing the memory required for

all morph agents. Indeed, the more available ob-

servations, the more accurate the linear transforma-

tion. However, the amount of memory increases as

the number of observations increases. As the required

memory has to stay at a ”reasonable” level, we pro-

pose to store in memory only the observations with

the highest error to adapt the coefﬁcients x and y.

Observations selection relies on the observation rele-

vance. Relevance is the difference between the princi-

pal data value and the value transformed by the morph

agent. From the three worst observations that it keeps

in memory, the agent computes the best coefﬁcients

to satisfy them. Then, it computes its new coefﬁcients

using a weighted sum of the old coefﬁcients and the

best ones. The weight used is ω ∈ [0.1, 0.9]. The

higher ω, the higher the impact on new coefﬁcients.

Using its new coefﬁcients a morph agent modiﬁes the

relevance of observations it has in memory.

This behaviour enables to observe two trends: ei-

ther the criticality decreases until it converges around

a low value with the augmentation of observed situ-

ations, either no convergence is observed. The ﬁrst

trend occurs when a linear relation exists between

both the data objective and the data agent. In other

words, a low criticality means that the morph agent

is able to transform the reference frame of its d

into

its d

. A morph agent unable to reduce its criticality

is considered useless and consequently, it destroys it-

self. However, to avoid inappropriate destruction be-

cause of a slow adaptation, a morph agent considers

itself useless only if its criticality is high and if an-

other morph agent from the same data agent has a

low criticality.

Group Agents. As different e

represent the

same d

, we introduced a third agent type called

group agent. A group agent is always linked to at

least one data agent. A group agent aims at having

the most reliable value to give to the Decision Process

DP and to reach this goal, it tries to regroup several

data agents together.

The environment of a group agent is composed of

all the other group agents of the environment, the data

agents linked to it and the DP. A group agent can ex-

ecute four types of action: 1) propose one of its data

agent to another group agent, 2) propose a merging

with another group agent, 3) exclude a data agent

and 4) send a value to a DP. A group agent eval-

uates its criticality using formula 3 according to the

adequacy of all its data agents. The adequacy of the

data agent da

with the data agent da

is the criti-

cality computed by the morph agent of da

. A group

agent tries to have as much data agents as possible.

The parameter δ ∈ [0.0,2.0] models the group agent

will to link a new data agent. The highest δ is, more

easy is to link a new data agent.

crit

∑

(i=0, j=1,i6= j)

(crit(ma

,da

) − δ)

nbDataAgentInGA

(3)

From this formula, the criticality of a group agent

strongly depends on which data agent is inside the

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

356

group. To lower its criticality, a group agent wants to

exclude high critical data agents, and enables those

data agents to be available to join another group.

Each group agent evaluates the impact of the integra-

tion of the proposed data agent from a criticality point

of view. If the integration reduces the group agent

criticality, the data agent is accepted. When no group

is willing to host the data agent, it is expelled and

associated with a new group agent. This new group

agent contains this unique data agent. However, as

adding a new low critical, data agent decreases the

overall criticality of the group agent, this latter is will-

ing to be linked to the most data agents possible in

order to reduce the impact of noisy information.

A group agent also has the possibility to merge

with another group agent if they both possess data

agents representing the same information. The merg-

ing decreases the adequacy of its data agents. How-

ever, it must not lead to the expulsion of one data

agent of the group. In the same way as the propo-

sition of hosting a data agent, two group agents can

evaluate if the resulting merging is less critical than

remaining separated.

Finally, a group agent aims to propose a value

to the DP. A group agent sends a proposition for an

input i of the DP. To increase the accuracy, the group

agent adapts the proposed value of each available data

agent for the input. A group agent associates a weight

with each data agent to which it is linked. Those

weights enable it to compute the value sent to the DP

according to equation 4.

value

∑

i=0

∗ da

∑

i=0

(4)

Once the value proposed to the DP, the group agent

receives the DP feedback which is the ideal value.

Then, the group agent adapts the weights to improve

its accuracy for the next time. Each data agent has

a noisy value. Some sensors overestimate, others un-

derestimate the same information and some sensors

are more accurate than others. A group agent adapts

the weight of each data agent according to the value

they have given. The most accurate data agents are

rewarded with an increase in their weight, while other

weights are reduced. In addition, a group agent in-

creases the weight of data agents whose value would

impact positively the group agent value if its weight

was higher. Equation 5 is used by a group agent to

modify the weight of its data agents. With α, β and γ

the parameters used to modify the evolution speed of

the weight and V the value of the entity (i for the data

agent, DP for the decision process and ga the group

agent). After several experiments exploring those pa-

rameters, the values α = 0.1, β = 0.05 and γ = 1/30

have been chosen.











+ α if |V

−V

| < V

−V

− γ if |V

−V

| > |V

−V

+ β if V

∈]V

[ or V

∈]V

[

(5)

3.3 Resolving Objectives through

Agents Interactions

In MAS, interaction between agents can lead to a

global phenomenon called emergence. This section

describes the emergence of the objectives from the in-

teractions of agents.

First Objective: Replacing Missing Data. This

ﬁrst objective is achieved thanks to interactions of

data and morph agents. The e

i, j

replacing a missing

is selected according to its adequacy with p

. After

several resolution cycles, we observe a reduction of

the morph agents number linked to a data agent. The

remaining morph agents achieve an accurate transfor-

mation of the reference frame. As a consequence, a

data agent is able to replace a missing p

still linked

to the morph agent.

Second Objective: Improving the Reliability of

Information. This second objective enables to over-

come the noisy data problem. As the data agent

can now translate its value into a different reference

frame, it is interesting to group together related e

and p

to extract the most accurate value from the

group so formed, thus taking advantage of the multi-

source information to reduce the noise. At the be-

ginning, group agents are, at the most, as many as

data agents. After several resolution cycles (cf sec-

tion 4.1), related data agents have a strong adequacy

and the group agents linked together merge. By con-

sequence, the number of group agents decreases and

the remaining group agents have more related data

agents. The value sent by a group agent to the DP is

the combination of all the linked data agents values.

4 EXPERIMENTATION

This section aims at evaluating the LUDA system

with a large number of experiments. Most used

datasets are synthetically generated to verify the lin-

ear transformation hypothesis and have a signiﬁcant

amount of different data. A real smart city dataset has

been modiﬁed using real translations to experiment on

real data.

A Self-adaptive Module for Cross-understanding in Heterogeneous MultiAgent Systems

357

4.1 Metrics

The efﬁciency of the LUDA method is evaluated us-

ing several metrics:

1. the accuracy of morph agents adaptation (the

transformed value is compared to the correct one);

2. the resulting group: complete or incomplete

group, misplaced or excluded data agent;

3. the impact of the values sent to the DP on the re-

sult of equation 1;

4. the time required to complete an adaptation cycle;

5. the number of remaining agents.

The different experiments consider several environ-

ments with various number of i) principal data and of

ii) extero data per principal data.

All the experiments last 200 cycles of resolution.

A resolution cycle consists in : 1) each group agent

computes a value to give to the DP; 2) each morph

agent receives the DP feedback and adapts its coefﬁ-

cients; 3) each group agent receives the feedback and

adapts the data agents weight and 4) group agents re-

organise themselves.

4.2 Experimentation with Noisy Data

Noise is added to each dataset using one of the three

Gaussian-based strategies : 1) every data is altered us-

ing a Gaussian noise with identical distribution; 2) the

Gaussian distribution is different for each data and 3)

the data value is always over or below the true value.

Similarities of Translation Functions. This sec-

tion assesses the effectiveness of the morph agent

learning. We compare our results to state-of-the-art

methods that are linear regression (LR), Random for-

est regression (RDM) and Gradient Boosting Regres-

sion (GRAD). Figure 2 shows the value difference

between the true data and data estimated with impu-

tation methods. For visualisation purposes, only 100

cycles are displayed. We observe that even if the error

is lower using LUDA, some peaks have arisen, which

has a negative impact on the overall result. A peak

represents a situation where a morph agent was not

efﬁcient to transform the value of its data agent.

Formed Groups. Table 1 shows the resulting

groups at different resolution cycles. Data is well-

placed when it shares the same group as their related

. A group is completed when all the data agents

representing the same information in various refer-

ence frames are grouped together. In an environment

with no noise or with same noise, group accuracy is

high. In case of different noise values, the noisiest

observations are stored in memory to be used for the

Figure 2: Difference between the true data and data esti-

mated with LUDA, LR, RDM and GRAD.

Table 1: Formed Groups result according to the percentage

of Completed Groups (CG) and Well-Placed Data (WPD).

Cycle 30 90 150 180

Metric CG WPD CG WPD CG WPD CG WPD

68 data

No noise

100 100 100 100 100 100 100 100

68 data

5% noise

88.24 92.65 88.26 95.59 94.11 95.59 94.11 98.53

68 data

Diff noise

70,59 85,29 88,24 97,06 64,71 88,24 64,71 85,29

192 data

Diff noise

75,51 90,10 73,47 94,27 73,47 91,15 71,43 90,63

392 data

Diff noise

73,74 89,54 75,76 92,86 76,77 92,09 78,79 93,62

adaptation. Noise degrades the efﬁciency of the ade-

quacy computation, leading to less efﬁcient agent in-

teractions and fusion.

Decision Process Result Difference. This section

explores the advantages of using several data using

the LUDA system in a noisy environment. Figure 3

shows the value difference between the principal data

and the one adapted by a group agent following equa-

tion 1. The effectiveness of LUDA compared to using

only known data depends on the noise nature (simi-

lar or different Gaussian, overestimation and under-

estimation). LUDA is more effective when different

sources underestimate or overestimate. The peaks ob-

served in ﬁgure 2 have a direct negative impact on the

DP result. A LUDA improvement would be to detect

when the value of a data agent is really far from those

proposed by the other data agents in the same group.

4.3 Exploration of the Computation

Time

To explore the computation time, experiments con-

sider the number of remaining agents compared to the

objective (metric 5 in 4.1), and the time to achieve a

resolution cycle.

Figures 4 and 5 show impact of the data number

on computation time. In the worst-case scenario, all

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

358

Figure 3: Effectiveness of LUDA on different noise values.

Figure 4: Evolution of the morph agents coefﬁcients adap-

tation.

data are available and each data agent has created a

morph agent to link it with a data agent. During the

coefﬁcients adaptation of morph agents, the destruc-

tion of useless morph agents decreases the computa-

tion time as seen in ﬁgure 4. A peak has arisen at

cycle 17 and then the number of remaining morph

agents reduces until the 35

cycle from which it is

stabilised.

In a real case with computation time constraints,

the number of data to process in one cycle should be

limited. A solution would be to give priority to crit-

ical morph agents while non-critical ones would not

adapt. Figure 6 assesses the effectiveness of destroy-

ing useless morph agents, on the computation time.

5 CONCLUSION AND

PERSPECTIVES

In this paper, we presented the MAS-based LUDA

module to address the cross-understanding in MAS.

Figure 5: Evolution of the morph agents number.

Figure 6: Difference between keeping morph agents and

enabling them to destroy themselves.

Heterogeneity of agents reference frame can be an

obstacle to the cooperation resolution of a MAS in

multi-sources context. Thanks to the interactions of

three types of agents, the LUDA module achieves

two objectives: 1) translate information from a ref-

erence frame to another one and 2) reduce the noise

by grouping similar information from different refer-

ence frames. The low number of observations enables

to add the LUDA module to an agent in operation.

We are currently working on using LUDA in the

real context of Connected and Autonomous Vehicles

(CAVs). Using LUDA in the Internal Decision Layer

of a CAV should enable it to understand any commu-

nication. The different reference frames used by each

vehicle constructor may impact communications be-

tween CAVs. In this context, LUDA could translate

a value communicated by a CAV and enable coopera-

tive strategies.

We plan to improve the morph agent learning al-

gorithm to enable it to learn non-linear function with

a small loss of effectiveness. The approximation of a

A Self-adaptive Module for Cross-understanding in Heterogeneous MultiAgent Systems

359

non-linear function in addition to several linear func-

tions is currently under study. A further study will

be conducted with three objectives: 1) improving

the adaptation of morph agents to include non-linear

transformations; 2) reducing the computation time of

the data agent displacement between groups by re-

ducing the necessity for a group to know every other

group; 3) reducing initial computation time by giving

a cooperative strategy to morph agents to select only

the one that needs adaptation the most. Furthermore,

group values adaptation can be improved by estimat-

ing the noise of each data agent.

ACKNOWLEDGEMENTS

This work is part of the neOCampus opera-

tion of the University Toulouse III Paul Sabatier.

www.neocampus.org

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