MASC: Map Sectors Creator

A Tool to Help at the Configuration of Multi-Agents Systems for Everyone

Aurélie Gaudieux

, Joël Kwan

, Yassine Gangat

and Rémy Courdier

Centre d’Economie et de Management de l’Océan Indien, University of Reunion Island, Saint-Denis, Reunion Island

Ecole Supérieure Ingénieur Réunion Océan Indien, University of Reunion Island, Saint-Denis, Reunion Island

Laboratoire d’Informatique et de Mathématiques, University of Reunion Island, Saint-Denis, Reunion Island

Keywords: Automation, Initialization, Configuration, MAP, Sketches, Plans, Cutting Out, Extraction, Grid, Bitmap

File, Code Snippet, Online, Web Based, User-Friendly.

Abstract: The initialization at the beginning of all type of simulation is recurring. In the field of multi-agents, spatial

environments on a micro or macro scale, or more abstract context, such as mapping out energy sources for

example, need often to be modelled. The initialization of these environments wherein evolve agents is

generally tedious and time-consuming depending on the fineness of the mesh of the cutting in case of a 2D

spatial representation. In this paper, we present MASC (Map Sectors Creator), a user-oriented tool that

permits easy initialization through the creation of a mesh directly usable in multi-agents systems simulations

platforms. The automation of cutting out maps followed by the generation of directly usable code snippets

optimize the working time on initialization and allow to focus on the simulations results and observations.

1 INTRODUCTION

Computer modelling is the formal description of a

system that allows manipulation of a virtual copy

using a computer. To obtain this virtual

representation, a model must be implemented. The

process from a theoretical context to a more concrete

one involves the production of codes to be used to

have a final product in the form of an application.

Before running a simulation, the initialization

must be settled, which includes configuring the

simulation context. Knowing that the context has a

huge impact on the proceedings and the outcomes of

the simulation, we often give more attention to data

in particular and starting environment. Each

configuration leads, so to speak, to an almost unique

experience depending on the configuration settings

(Gangat, et al., 2009).

Optimization of working time issue arises when

handling complex systems, especially in multi-

agents systems where more attention must be paid to

data and different proceedings performed during

iterations.

To help solving this problem, we propose MASC

– Map Sectors Creator, an easy to use tool that can

provide an environment base represented by cutting

a base map, a diagram or a plan into a grid of sectors

directly usable in multi-agents simulation platforms

such as NetLogo (Wilensky, 1999) or the platform

of multi-agent simulation of IREMIA laboratory at

the University of Reunion Island, GEAMAS-NG

(GEneric Architecture for MultiAgent Simulations)

(Payet, et al., 2006).

Thus, MASC permits to save time and focus on

implementation of models. In addition, MASC

provides a simplified user experience through

opportunities brought by recent developments in

Web technologies. We want to propose a user-

friendly tool that could help anyone to optimize

working time and facilitate the configuration of the

initialization. The word “masc” means “men” in

Portuguese.

This kind of tool is useful for several simulation

domains for example geographical, economic,

meteorological or epidemic ones.

2 MASC

2.1 The Need

In the context of models based on real existing ones,

we perform simulations in which agents operate on a

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Gaudieux A., Kwan J., Gangat Y. and Courdier R..

MASC: Map Sectors Creator - A Tool to Help at the Conﬁguration of Multi-Agents Systems for Everyone.

DOI: 10.5220/0005133408360844

In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (MSCCEC-2014), pages

836-844

ISBN: 978-989-758-038-3

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: Using MASC for configuring multiple experimentations of a simulation.

space generated from spatial division of maps. E.g.:

SIEGMAS (Gaudieux, et al., 2014).

Some existing tools already provide some parts

of solution to this problem. We can take the example

of XELOC Language, eXtensible Editing Language

Of Configuration, which is a support for the

configuration an initialization of multi-agents

systems. XELOC is based on the basics of XML and

requires the handlings of another programing

language. One of MASC objective is to repeal both

the acquisition processes and handling of a new tool.

The development of a tool like MASC was

driven by mainly two reasons.

Firstly, the need of a tool allowing the

automation of the process of cutting out maps to

perform multiple simulations of a same model with

different geographical areas. The tool aims to

facilitate the transition from map into an exploitable

representation. It is very interesting to incorporate

modelling with different configurations. Some

models can lead to significant results from the

abstract level to a less abstract one.

And secondly, the use of a user-friendly tool also

permits to effortlessly make rather redundant tasks

of creating an environment where agent evolves

along different simulations. Several configurations

can then be produced more efficiently in no time as

revealed in Figure 1.

2.2 The Concept

MASC has been designed to increase productivity

on implementing a model so that it provides a

streamlined user experience. That is why the tool

was developed in order to ask the fewest operations

and minimum familiarization while providing

enough controls. The user interactions with MASC

interface are then based on the daily operations

being done by any computer user (drag and drop,

file uploading, form filling, mouse clicking). The

graphical user interface (Figure 2) is quite simple

and reduces the displayed information to only the

useful ones.

Figure 2: MASC GUI.

The quantity of requested information to input is

also kept to a minimum, the rest being automatically

filled by calculations.

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A question arose as to which file types would be

supported by the tools. The most used type of output

images are vector graphic files generated from GIS

softwares or bitmap files for several maps, plans,

drawings, sketches, or other digital copies created

from paper versions. Knowing that it is much less

restrictive to convert a vector image into bitmap on

to the opposite direction, we have decided that the

first versions of the tool would only support bitmap

images in input.

Moreover, we wanted the tool be designed so

that it is usable at any time by anyone with any

computer without altering the workspace of the user.

That is why we chose to make an online web based

application.

Regardless the necessity of a connection to the

server, the downside of this type of application is

that the power of calculation is limited by the

capacity of the hosting server and the technology

used for the different calculations and the allowed

uploading file size. For the first version of our

application, we chose to make it deployable to a

maximum of easily accessible servers. So we

deployed it on a basic server provided by a common

hosting plan. And for a maximum compatibility with

available programming languages on these type of

hosting, we used PHP programming language for the

server side coupled with HTML5 and JavaScript for

the client side.

On the upside, anyone can access the application

with a simple connection to the server and a recent

Internet browser. In a future perspective, the server

side will be switched to a Java version that will have

a better performance calculations but require an

upgrade of the hosting plan.

2.3 Presentation

2.3.1 The Ease of using MASC

Using MASC is divided around three main steps.

The first step consists of inputting all the necessary

information needed by the tool to correctly extract a

cutting grid space from an image that is presented to

it. This stage takes place mainly on the client side

that is to say on the user’s Internet browser (shown

highlighted in Figure 3). The information required

for the proper functioning of the tool are:

 A bitmap image file in JPEG, PNG, GIF, or

TIFF format, which is a representation of the

space where agents evolve during the

simulations.

 The size of the grid is simply summarized by

the number of sectors to cut out across the

width of the image. Because we are working

with a grid where sectors are square shaped,

the number of sectors in the height direction

can be automatically calculated.

 A list of colors selected from the preview of

the image taken by clicking on it.

Figure 3: Details of MASC workflow.

In a second time, these three data are sent to the

server. Then the script on the server can manipulate

the bitmap file and can extract a grid that will serve

as a starting point for generating the code snippet

used for initialization of simulations. At this stage,

the user will have no operations to do, all the

treatments will be done in autonomy at the server

level.

In the last step, new information resulting from

the extraction made on the server side is displayed

graphically to the user using the preview area of the

graphical user interface displayed on his browser.

On the one hand, the user has a preview and on the

other hand he can slightly edit the grid before final

code snippet generation according to the chosen

programming language. At this level, the user can

still change the color of the different sectors by

clicking on them.

If needed, the user can change the number of

given sectors across the width to change crisscross

scaling and then regenerate the code snippet.

2.3.2 Colors

Always with the idea of putting the user at the center

of the experience and increasing the productivity in

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mind, MASC uses a list of colors selected from the

image by the user. This list is used in the second

step. It also allows to filter unwanted colors and to

avoid to keep annotations that are often merged on

the image itself. So the user only select interesting

colors.

Figure 4: Colors interpolation.

When extracting the colors, the image is divided

into sectors according to the given cutting grid. Then

each pixel is examined to determine if the pixel

color matches with one of the colors in the list. If it

is the case, the counter of the matching color in the

sector of the pixel is incremented.

In this process, the tool tolerates a degree of

shade. Thus, the color alterations brought by the

image compression does not prevent MASC to

correctly extract the desired colors in each area.

An array of colors grouped by sector is gradually

created regarding the progress of the review of the

image pixels. Then, the dominant color is selected to

be the color of the sector (Figure 4). By proceeding

pixel by pixel, the precision of the interpolation that

assesses a sector color is dependent on the cutting

out grid size. The more sectors across the width is

given in input, the greater the precision will be.

There is no minimum grid resolution, it all depends

on the goal and the need of the model.

To allow a certain degree of freedom and to

leave the choice of colors to prioritize on sectors, the

user can click on the preview grid cells to choose the

dominant color regarding the listed ones in the

working sector. These modifications are stored in

the client side to avoid the useless data transfers

between client and server. So directly after the

adjustments brought by the user, the code snippet

containing the representation of the space can be

generated.

3 CASES STUDY

The team being at work on two ongoing projects.

SIEGMAS which use the simulation platform

NetLogo and EDMMAS, which use the GEAMAS-

NG, we would like to test the relevance of MASC in

these projects. In this part of the paper, we will

explain the advantage of using MASC with theses

example.

3.1 SIEGMAS

SIEGMAS (Stakeholders Interactions in

Environmental Governance by a Multi-Agent

System) is a modelling conceptualized in order to

study the deviances of the farmers against the

forester and natural resources protected by the

management's transfers or another text of law.

3.1.1 Context

In the context of this project, the economical and

classic steps being limited enough for the

conceptualization of the individuals' behaviors and

the process of the common pool resources, a process

of multi-agents systems allows some improvements

in the study of these phenomenon and the creation of

a sales tool for the decision

This modelling has been conceptualized in order

to study the deviances of the farmers against the

forester and natural resources protected by the

management's transfers or another text of law.

Therefore, this simulation allows to identify the

interactions between the actors of the governance

taking part in the management's transfers and to

propose some targeted measures in agreement being

able to reduce the merging anomalies. Thus, this

model reveals that the knowledge of the sanctions by

farmers does not prevent them to infringe the

legislative measures of protection of natural

resources coming from the management's transfers

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or located in some few protected areas. (Brandouy,

et al., 2012).

3.1.2 SIEGMAS

Farmers (deviant or not) generally aim to make their

land as profitable as possible to cover several costs

(e.g. investment, maintenance). A farmer can choose

to spread legally his cultivation via the purchase of

new plots or reaching the legally maximum allowed

use of natural resources. In fact, the management's

transfers grant the farmer through the basis

community some right of cultivation of some natural

resources. However, the farmer can be a deviant by

farming illegally some plots of natural and forester

resources.

This model is mainly focused on the farmers and

the government and their interactions in relation to

the forester and natural resources as well as the

resources produced on the farms.

The model SIEGMAS has been implemented

under the platform of the programming called

NetLogo. The farmers can respect or infringe the

legislation towards the protection of natural

resources. The controls or the rupture can identify

the transgressions. Authorities grant some individual

sanctions to the deviants. Thus, not any collective

sanction impacts the respectful farmers of the

legislation. The farmers set themselves up in order to

increase their productivity and to perpetuate the trust

that the state authorities grant (or to avoid the

sanctions for everything by having a nearby context

in favor of their activity (Wade, 1987).

For the sanctions, the deviants want to exploit

randomly the lands being around his exploitations,

even those created illegally. At each iteration, the

government notes a random number of lands

illegally exploited and looks for the one who created

the exploitation. After launching several times the

simulation with different values of configurations, it

appears by observing the variation of the rate of

deviants that if the government controls the

management's transfers correctly, the sanctions are

well applied. The presence of the deviants increases

land deterioration. Some deviants however manage

to avoid sanctions and run some untaxed incomes.

Nevertheless, the application of a sanction to the

deviants rebalances the system so that the deviants

cannot get any profits from the illegal practice of

agriculture or the exploitation of natural resources.

Therefore, individual behaviors impact the collective

behaviors if a farmer knows the machinations of his

neighboring. The farmer generally grants his trust to

his neighbor as long as it doesn’t involve being a

deviant. However, if he knows the acts of deviance

of his neighbor, he will be more suspicious against

him and will denounce him.

This modelling is quite difficult to implement

and require a lot attention. For testing the model,

several configurations will be applied to multiple

simulations. Usually, these tasks take a lot of time to

prepare. Not to waste time, the creation of these

initial states should be automated.

3.1.3 MASC with SIEGMAS

In this case study, we are trying to model the

interactions between stakeholders in a precise

geographical area. To do so we have to create a

representation of a region in a simulation platform.

In this specific example, the region of Analamanga

(Madagascar) which directly usable map data are

quite difficult to find, is used. So we go from a map

extracted from a digital document that was provided

to us (Figure 5).

Figure 5: Analamanga region map.

Without MASC, we would have been constraint

to crisscross the image in software image processing

and then to manually select and assess the color of

each sector of the grid. Thanks to MASC, these tasks

were simply handled by giving the image file, the

number of sectors to cut in the width direction and a

color swatches that we simply took from the legend

of the map by clicking on it. Afterwards, a preview

was displayed to us (Figure 6). We decided then to

prioritize the colors of the sectors or not and

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generate the code snippet regarding the chosen

programming language, in this case NetLogo.

Figure 6: Preview of Analamanga region map generated

by MASC.

Part of generated code snippet for Analamanga

region map:

; Permits to create a representation of

the map

to create-world

clear-all

set-patch-size 20

resize-world 0 39 0 50

ask patch 0 50 [

set pcolor [186 158 74

]

ask patch 1 50 [

set pcolor [186 158 74]

]

ask patch 2 50 [

set pcolor [255 252 217]

]

ask patch 3 50 [

set pcolor [255 252 217]

]

[...]

ask patch 36 0 [

set pcolor [255 255 255]

]

ask patch 37 0 [

set pcolor [255 255 255]

]

ask patch 38 0 [

set pcolor [255 255 255]

]

ask patch 39 0 [

set pcolor [255 255 255]

]

end

The advantage of using MASC in this context

was to more or less finely crisscross the map to

translate from the bitmap file to an exploitable

representation of this map inside a simulation

platform. As a result we can have easily different

grid resolutions of the map with no effort and can

deploy rapidly different base maps for different

configurations.

This methodology also allows model

transposition to different geographical regions, for

different simulations executions on Reunion Island

and Mauritius regions and comparing obtained

results.

3.2 EDMMAS

EDMMAS (Energy Demand Management by Multi-

Agent Simulation) is a prototype based on the model

of the same name for simulating the production and

consummation of energy according to the evolution

of the population over the years.

3.2.1 Context

The issue in the EDMASS project is the energy

development of a territory using a reproducible

modelling. Reunion Island provides a favorable

research field in this area thanks to its isolations

because its small area (2.500 km²) promotes the

quest of new forms of energy development and

prohibits electricity production from nuclear power.

EDMMAS is a tool for helping experts to make

decisions through ABS, an overview of the

evolution of electricity consumption by 2030 and to

provide new facilities.

3.2.2 EDMMAS

EDMMAS is and evolution of DS model used to

explore various scenarios of land usages and

conservation planning that simulates on the whole

island, the interactions between the three types of

land use (natural, agricultural and urban), in order to

allow observation of the changes induced. The

energy layer has been added to allow this (Figure 7).

MASC:MapSectorsCreator-ATooltoHelpattheConfigurationofMulti-AgentsSystemsforEveryone

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Figure 7: EDMMAS dynamics.

EDMMAS was designed on the GEAMAS-NG

simulation platform. This platform has been created

with several powerful tools to reduce the gap that

lies between the real and complex system model.

Among the innovations of this platform, we notice in

particular XELOC. XELOC is a support for

configuration and initialization of multi-agent

systems. It is established on the basis of XML,

enriched by a semantic script language. Thus, while

a configuration for defining the form of a

conventional XML description, XELOC offers the

possibility of synthesizing a complex configuration

by means of a dynamic process. In addition, XELOC

incorporates a rich initialization technique using

semantic maps provided by mathematicians. All this

makes XELOC, scalable and accessible by nature, a

generic support used in many contexts of

implementation. A XELOC interpreter has been

developed within the GEAMAS-NG platform.

3.2.3 MASC with EDMMAS

In EDMMAS, we handle maps containing

information on energy sources available in Reunion

Island. We tried to use MASC to complement

XELOC Language and facilitate simulation

initialization.

Figure 8: Reunion Island average annual global radiation

(2009).

We take a map of average annual global

radiation to the horizontal of Reunion Island in 2009

(Figure 8) for the practical case.

Using MASC, we can easily retrieve a XELOC

code snippet corresponding to the map with the

correct color codes without using multiple tools.

When we manipulate codes, we usually don’t want

to lose time with redundant tasks such as picking all

wanted color codes in an image. Thus we benefit of

using advantages of MASC and XELOC Language.

MASC allows color selection and generates

effortlessly code snippets in XELOC language.

Part of generated XELOC code snippet of the

example:

<forMap name="OurMapName"

url="./myMapFile.jpg"

resolution="16">

<label name="rgba(255,18,56)"

r="255" g="18" b="56" />

[...]

<label name="rgba(229,255,0)"

r="229" g="255" b="0" />

</legend>

<cell legend="10classes"

operator="average"

mode="closer">

</case>

[...]

</case>

</cell>

</forMap>

Figure 9: Preview generated by MASC of the average

annual global radiation of Reunion Island.

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Using MASC for generating XELOC code

snippets does not require to send the image on server

for a post treatment. In this case, MASC is just an

advanced color picker which allows to generate

XELOC code snippet as output. We also note that if

we need the reuse of this map for NetLogo, we have

just to set the grid resolution, ask for extraction, and

then change the language choice and regenerate the

code snippet that enables us to have an equivalent

map in patches for NetLogo (Figure 9).

Part of generated NetLogo code snippet for

Reunion Island average annual radiation (2009):

;Permits to create a representation of

the map

to create-world

clear-all

set-patch-size 20

resize-world 0 59 0 44

[...]

ask patch 19 44 [

set pcolor [254 91 117]

]

ask patch 20 44 [

set pcolor [254 91 117]

]

ask patch 21 44 [

set pcolor [254 122 135]

]

[...]

ask patch 59 0 [

set pcolor [255 255 255]

]

end

Figure 10: Reunion Island power plants (2007).

We notice in this example of type of use that an

advanced version of MASC would be very useful if

the tool can also permit to set the location of some

agents on the map. In the context of EDMMAS, we

can take for example the localization of power plants

in Reunion Island (Figure 10) that will be the agents

of the model. Keeping in mind that MASC is

designed to facilitate the configuration of

simulations, one of the future goals of MASC is to

allow the placement of agents in addition of creating

the environment in which they evolve.

4 FUTURE WORKS

MASC is already a fully functional tool and

responds more or less to the initial requirements

mentioned in above parts of this paper. However,

during our experiencing phases, we note that the tool

can be improved to improve its effectiveness.

On the server side, an upgrade of the hosting

plan would permit a usage of another programming

language for example Java which allows to have a

compiled server side application to improve the

calculation time instead of an interpreted script.

With Java, we could also ignore the passage of data

through Apache server and prevent the flow of

information between several services. The client side

will directly communicate with the server side of the

application.

The client side can also be optimized by

modifying some functions and not using frameworks

as it is now. The advantage of using frameworks is

the time gained at developing the tools. Once the

proof of concept is acquired, the next step of MASC

is optimizations.

Then we can also add more feature on this tool.

Actually, the tools only manipulate the space of the

simulations. It could also permit to prepare the

initialization of multi-agents such as the positioning,

setting up clusters of patches or boundaries. We

could also expand the list of available multi-agents

simulation platform languages to generalize the use

of the tool and permit more members of the

community of the domain of multi-agent systems to

use it.

5 PERSPECTIVES AND

CONCLUSIONS

The redundant problem of creating representation of

a space in multi-agents systems leads us to find

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some useful tools to be more productive. We often

use maps from GIS softwares, paper supports and

other electronic documents that we want to set up in

our simulations.

In this article, we presented MASC, a support

tool for configuring multi-agent systems based on

the information extracted from colors of bitmap

images. MASC aims to provide a simplified user-

friendly service for the acquisition of code snippets

ready to use with multi-agent systems simulations

platforms. Thereby, creating multiple configurations

for a same modelling can be done quickly and

easily, while allowing the users to focus on the data

and the results of simulations. Moreover, MASC

allows to effortlessly create several of more or less

finely crisscrossed map for observing the evolution

of agents on different scales and precisions. We

could gain more time on configuration if the tool

permits also the placement of the agents if needed.

Our purpose in this article was not to bring out

the possibility to automate part of the

implementation of a modelling, but rather to

highlight the lack of user-friendly tools to simplify

the initialization of a simulation, which we assessed

in example and present to community a more or less

adequate solution.

REFERENCES

Brandouy, O., Mathieu, P. and Venryzhenko, I., 2012.

Optimal Portfolio Diversification ? A multi-agent

ecological competition analysis. PPAMS (Special

Sessions), pp. 323-331.

David, D., Payet, D., Courdier, R. and Gangat, Y., 2009.

XELOC : un support générique pour la configuration

et l’initialisation de systèmes multi-agents. Journées

Francophones Systèmes MultiAgents, JFSMA’09.

Gangat, Y., 2013. Architecture Agent pour la modélisation

et simulation de systèmes complexes multidynamiques

: Une approche multicomportementale basée sur le

pattern "Agent MVC", Saint-Denis: s.n.

Gangat, Y., Courdier, R. and Payet, D., 2009.

Démonstration : Aménagement énergétique d’un

territoire - une approche par simulation multi-agents.

Journées Francophones Systèmes Multi-Agents

(JFSMA), p. 237–240.

Gaudieux, A., Gangat, Y., Kwan, J. and Courdier, R.,

2014. Multi-agents model for the study of interactions

between the stakeholders in the common pool

resources : application to the district of Miarinarivo (

Madagascar).

Payet, D., Courdier, R., Sebastien, N. and

Ralambondrainy, T., 2006. Environment as support for

simplification, reuse and integration of processes in

spatial. MAS. IEEE International Conference on

Information Reuse Integration, p. 127–131.

Wade, R., 1987. The management of common property

resources : collective action as an alternative to

privatisation or state régulation. Cambridge Journal of

Economics, Volume 11, pp. 95-106.

Wilensky, U., 1999. NetLogo. Center for Connected

Learning and Computer-Based Modeling.

Northwestern University, Evanston, IL.

APPENDIX

A version of MASC is in public access at

http://labs.jkwan.com/masc/.

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