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Methodology for a New Agent Architecture Based on the MVC Pattern


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In the last few years, the multiagent system’s paradigm has been more and more used in various fields, specially in the simulation field (MAS). Whenever a new application came into being and has been validated by its review board, specialists usually want to reuse it, fully or partially, in order to cut down the time and price of developing similar application. But this reuse is not as simple as expected. In a previous article, we proposed the DOM modeling to tackle modeling difficulties which arise in a complex system. However this solution has its limits as we will develop here. In this paper, we define a more complete agent modeling, based on the MVC design pattern, in order to to push back these limits.
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Methodology for a New Agent Architecture
Based on the MVC Pattern
Yassine Gangat, Denis Payet, and R´emy Courdier
University of La R´eunion, LIM
Saint-Denis, Island of La R´eunion
Abstract. In the last few years, the multiagent system’s paradigm has
been more and more used in various fields, specially in the simulation
field (MAS). Whenever a new application came into being and has been
validated by its review board, specialists usually want to reuse it, fully
or partially, in order to cut down the time and price of developing similar
But this reuse is not as simple as expected. In a previous article, we
proposed the DOM modeling to tackle modeling difficulties which arise in
a complex system. However this solution has its limits as we will develop
here. In this paper, we define a more complete agent modeling, based on
the MVC design pattern, in order to to push back these limits.
Keywords: multi-agent, behaviors, MVC, aMVC, design pattern.
1 Introduction
1.1 Context
In the last few years, the multiagent system’s (MAS) paradigm has been more
and more used in various fields, especially in the simulation field. While some
applications are used for pedagogic purposes, others are made in order to pro-
vide decision-support tool, e-commerce application, etc. which implies a wide
range of complexity’s level. Like any new paradigm, the MAS’s wide-spreading
requires new models, new methodologies and new softwares to support develop-
ment engineers with robust and reliable applications.
Whenever a new application came into being and has been validated by its
review board, specialists usually want to reuse it, fully or partially, in order to
cut down the time and price of developing similar application. But this reuse is
not as simple as expected.
The idea of Dynamic-Oriented Modeling (DOM) [1] was also born of this de-
sire of model’s reusing. In that previous article we proposed the DOM modeling
to tackle modeling difficulties which arise in a complex system. These difficulties
mainly consist in the fact that multiagents systems are becoming more and more
hard to model due to the complexity of the system studied. After having imple-
mented DOM on multiple application, we realized that DOM was not enough to
A. Ramsay and G. Agre (Eds.): AIMSA 2012, LNAI 7557, pp. 230–239, 2012.
Springer-Verlag Berlin Heidelberg 2012
Methodology for a New Agent Architecture Based on the MVC Pattern 231
overcome all of these difficulties, especially because it doesn’t care about agents’
Indeed, to complete this one, in this paper, we study some example and pro-
pose a new behavioral model based on the well-attested MVC model. It is an
original reuse of this well know pattern to obtain a new modelization approach
for modularizing not only the development of environment but the development
of agents as well.
As such, we first start by breaking up an agent according to some axis. Then
we will recompose this splitted agent in a MVC-like pattern (aMVC) to define
our new modeling proposal: the Multi-Behaviors Modelization.
Lastly, we conclude by giving a few perspectives of this work.
2 From the Breaking Up of an Agent...
This new approach called Multi-Behaviors Modelization is based on the splitting
of the agent into severals pieces. The first step has already been presented in a
previous paper [1]. After that first one, the next steps are phases that will allow
us to build an MVC based agent.
2.1 Splitting of the Agent According to Dynamics
When we say ”splitting of the agent according to dynamics”, we mean environ-
ment splitting reflected on the agents. In a previous paper [1], we have presented
a modeling method DOM (Dynamic-Oriented Modeling) based on dynamics,
where we started with a basic agent (c.f. Figure 1a).
A”dynamic” is an association of a set of activities that participate in a
major characteristic of a complex problem.
The aim of this modeling method is to break down a complex problem into
some less complex parts (i.e. dynamics), using the environment (which is the
location where agents evolve) as the coupling element for these dynamics in the
Figure 1b.
In a few words, DOM is based on the integration of several layers called Mono-
Dynamic Model (MDM), where each layer is related to a specific activity (such
as population evolution, or flow of energy), into a multi-MDM model.
Let’s take a little fictive illustrating study case: We want to make an Agent-
Based Simulation (ABS) about wolves (and other animals). We will focus our-
selves on wolves. A wolf agent will have some states, behaviors and interactors
with environments. The first split we are going to make will through dynamic.
We can identify two majors characteristics in this problem: the ”individual dy-
namic” (that will include everything related to the individual such as its emotion,
health, age, etc.) and the ”team dynamic” (that will include everything related
to the pack of wolves such as its rank, hunting, migrating, etc.).
This DOM methodology has already been applied in previous project such as
DS [2] and EDMMAS [3]. The feedback we had on this application showed us
that using DOM was a good choice. Indeed it enabled us to easily reuse an ”old”
simulation and build a new one from it.
232 Y. Gangat, D. Payet, and R. Courdier
(a) Basic agent (b) Splitting of the agent according to
dynamics and its effect on the agent
Fig. 1. From Basic Agent to the first splitting step
But the DOM methodology, based on the environment splitting, was not
enough to fully apprehend modelization of complex system. We are facing some
underlying problems that are general to every case of reusability. Whenever
someone wants to reuse a MAS model, especially its agents, he will breast the
problem of agents’ behaviors. Despite the fact that dynamics had been separated,
having the agents’ states and its behaviors at the same level doesn’t facilitate
the reusability.
Indeed, DOM does not take into account the agents’ behaviors, which is a
critical point (as we can see in [4]) if we want to improve the reusability of
our model. In this previous article, we presented a collaborative method and a
NetLogo prototype focused on green turtles in the South-West Indian Ocean and
we showed that each expert has different ways of modeling the turtles according
to its interactions with the wind, the surface temperature, the stream, etc.Thus,
because DOM concentrates its methodology on the environment and cannot
tackle the problem of agents’ behaviors, we also need to consider a methodology
for the agents’ modeling, especially its behaviors.
2.2 Splitting of the Agent According to Realms
In order to detach ourselves from ambiguous words, we will introduce the world
realm” which means area of behavioral expertise. Each set (states and behav-
iors) can be split according to the ”realm” to which they are referring (i.e. in
the Figure 2a the A realm, the B realm, the C realm where C is a bit related to
B, and the D realm with two different behaviors).
Methodology for a New Agent Architecture Based on the MVC Pattern 233
(a) Splitting of the agent according to
dynamics and realms
(b) Splitting of the agent according to its
three components, realms and dynamics
Fig. 2. The second and third splitting steps
The ”inside” of the agent in the Figure 2a shows us a certain amount of
subsets (that could be really huge according to the complexity of the system)
that are linked together. Our goal is precisely to organize everything to ease its
The difference between realm and dynamic is that a dynamic can be com-
posed by one or more realms. For example, the ”social”’s realm and the ”posi-
tion”’s realm is part of the ”communication”’s dynamic. Another example, the
dynamic of ”energy evolution” is composed by three realms : production of en-
ergy by plants, consumption of energy by residential houses and consumption
by factories.
In our previous study case, we would be able to split the agent according to
three realms: Emotional Wolf realm, Survival Wolf realm (both included in the
”individual dynamic”) and the Social Wolf realm (included in the ”team dy-
namic”). We can notice, that realms of other animals can be included in the
same dynamics (e.g. Survival Moose realm in the ”individual dynamic”).
2.3 Splitting of the Agent According to Its Three Components
An agent can be identified by three components:
The agent’s states, which contains its attributes.
The agent’s behaviors, which organizes all the actions it can undertake (de-
cisional process).
234 Y. Gangat, D. Payet, and R. Courdier
The agent’s interactors, which allows interaction (influence and perception)
with the environment.
In this new approach, we are taking an extra step in our initial DOM partition,
in order to separate behaviors to free experts from behaviors unfamiliar to them.
In this structure, we will define the whole ”world” as an aggregation of several
layers of physiognomy (LP), several layers of behaviors (LB ) and interactors
(Influence and Perception).
From this splitting by realms, we then can also split every agents and put
them in differents physiognomies and behaviors layers.
If we take the same example, it will be as follows (in the Figure 2b):
4 Layers of physiognomies LPA,LP B,LP Cand LPD.
5 Layers of behaviors LBA,LBB,LB C,LB D1and LBD2.
10 Iteractors for each agent: 5 for Influence and 5 for Perception
This cutting of the agent into realms allows us to complete the one obtained by
DOM [1] in term of dynamics. In this example, if we supposed that the splitting
will result into three dynamics (Dyn AB ,DynCand DynD), their relation will be
like in Figure 2b. In this figure, we can see the differents layers of the system,
illustrated with the splitting of one agent. If we have hundreds of agents of the
same kind, the same layers will be shared amongst them.
Note: In Figure 2b, we choose to simplify by showing the splitting of only one
agent; but in fact, every agent will be split by the same realm and sent to the
adequate layers. It could be represented as in Figure 3a. The advantage of this
technique is that usually in a complex system, there are many agents that can be
categorized by ”kind”. Each ”kind” will be defined by the same set of behaviors,
e.g. behaviors of an Omega wolf will be the same for every Omega wolf. By taking
behaviors away from the agent’s state, we are then able to factorize behaviors
and reduce the complexity of the model.
Layers of Behaviors. One behavior’s layer consists in definition of agents
behaviors related to one realm.
A layer of behaviors is not supposed to contain the whole behavior of the agent,
but its behavior related to one realm of the complex system. It could be defined
by known method such as: logic definition, hard-coded definition, color-coded
definition, formularized definition, Turing machine definition, tabular definition,
matrix definition, etc.
These are only few examples, but it could use and combine a wide set of
modelization methods, depending on the way the expert wants to model in his
Layers of Physiognomies. The physiognomy’s layers is in fact a set of dy-
namic states related to one particular field (c.f. Figure 1a and Figure 2b). We
used the word ”physiognomy” in order to express the ”character” or ”person-
ality” of the agents (its states and some internal laws related to this field) but
Methodology for a New Agent Architecture Based on the MVC Pattern 235
not what is usually called ”body”, because we did not incorporate the capacity
of interaction here (which is usually associated to the body).
As you can see, an agent’s states related to a particular realm (such as C)
can influence and be modified by behaviors of the same agents, but related to an
another realm (such as B). Moreover, a unique layer of physiognomy (i.e. LPD)
can be linked to two or more layers of behaviors (such as LBD1and LB D2).
In our previous study case, let’s try this three components’ split on the Survival
Wolf realm. It would result in a set of physiognomies (such as its health, stamina,
age, hunger, etc.), behaviors (attacking, calling for help, patroling, etc.)and
interactors (walking, running, biting, etc.). Wolves are usually not hunting when
they are hungry, they usually call their pack in order to organize an attack. This
realm is therefore related to the Social Wolf realm.
3 the MVC Agent
3.1 Introduction to Design Patterns
Since the introduction of patterns by Christopher Alexander in 1977-1979 in the
architectural concept field, the idea of design patterns in software development
started in 1987 [5] and gained popularity in 1994 after the book of [6].
Design patterns encourage reusability and can be used as ”building blocks”
for complex software. Several researches towards the reuse of model have been
made in various fields as Software Engineering but also in Artificial Intelligence,
Aridor and Lange’s paper [7] was one of the first pioneer in applying design
patterns to the MAS field. Ideas are emerging like PASSI (Process for Agent
Societies Specification and Implementation) [8]. Since, several research has been
done as resumed by [9], but most of the work has been focused on patterns for
agent-oriented software or for the agent’s interaction. Moreover, when the pro-
posal is a pattern-based design methodology, the proposed patterns are usually
homemade or specific to one domain. As stated by [10] to maximize the bene-
fits of design patterns, they should be applied uniformly throughout the MAS
research community, that would result in spreading MAS solutions and giving
valuable feedback to the MAS research community.
As mentioned by [11] and [12], in order to create sets of system components
needed to support highly interactive graphical software development, the MVC
strategy has been chosen. Isolating components from each other as much as
possible helps the application designer to understand and modify each particular
unit, without having to know everything about the others.
3.2 Model-View-Controller
If we get back to the basic concept of the MVC paradigm [11], we will see that
the view manages the graphical and/or textual output to the portion of the
bitmapped display that is allocated to its application. The controller interprets
236 Y. Gangat, D. Payet, and R. Courdier
Table 1. Example of MVC in a GUI component
Designation Button in Swing
Model ButtonModel
View ButtonUI’s visual representation
Controller ButtonUI’s handlers
the mouse and keyboard inputs from the user, commanding the model and/or
the view to change as appropriate. Finally, the model manages the behavior
and data of the application domain, responds to requests for information about
its state (usually from the view), and responds to instructions to change state
(usually from the controller).
MVC implementation in smalltalk [11] inspired many other GUI frameworks.
As an example, if we consider the Button class in Swing (Java [13]), the class
which is used to represent a simple push button, we could see that Swing uses a
variant MVC (where V and C are linked together). The Button class (see Table 1)
is associated with a ButtonModel implementor for the model. It encapsulates
the internal state of a button and defines its behaviors. Button classisalso
associated to a ButtonUI for its view, and possibly one or more event handlers
for its controller.
Nearly all of the complex GUI elements in Swing use the component-level form
of the MVC pattern for a number of excellent reasons, but the most important
for us here is that it’s highly reusable and it’s easier to customize a component
and link the components together.
3.3 Applying MVC to MAS: aMVC
If we apply this pattern to our MAS of a complex system , we will benefit most
of the advantage of the MVC pattern. In order to head toward this, we need a
new approach, starting from the bottom. The aim of our discussion is not only
to propose a well-known design pattern, that could easily be both comprehended
by experts and implemented by any developers. But we also want to underline
the parallel between this design pattern and our modelization.
It’s important to note that we are not talking about the software’s architecture
(as we can see in [14–16]) or a methodology in order to help in determining the
types of agents needed to build successful MAS (such as in [17]), but about
the agent’s architecture itself. Applying MVC software’s architecture would
”simply” consist in using MVC in the context of software engineering, e.g. in a
platform it will be separating visualisation of the world from the internal ABS’s
engine, usually through Object-Oriented Programming (OOP). Here, we want
to import a methodology (MVC) existing in OOP into the world of MAS and
use it at as a design methodology for the agent. In a nutshell, we are going to
use an aMVC (agent MVC) pattern.
Methodology for a New Agent Architecture Based on the MVC Pattern 237
Table 2. One layer of an aMVC agent
Designation Agent in one realm X
Model its states & internal laws of X
View its interactors with the environ-
ment related to X
Controller its behaviors in X
We have to ascertain identity of each concept in our current model: Who is
the model ? Who is the view ? Who is the controller? In order to do that, we
will talk for the next three sections of an agent related to one particular realm.
Defining the Model. In our modelization, the states’ collection mixed with
internal evolution laws related to the realm (such as aging of an agent) should be
the model. We usually tag model merely as a database in Software Engineer-
ing; but the model in MVC is both the data and the domain logic needed to
manipulate the data. Thus, identifying this to be the model is a good solution:
LP is a subset of dynamical data (states of agents) which evolves with time due
to LBs and internals laws of the realms (that make the consistency of the data).
Defining the Controller. By adopting this point of view, we have identified
the controller : the behaviors. The behaviors manage the agent’s interaction
with the environment and, for this, use its states: either in order to consult the
states to take a decision or to influence its modification in order to memorize
any experience learnings.
Defining the View. Now, last but not least, the view has to be defined.In
other words, we can say that an agent’s perception is similar to a button or a
checkbox (in a graphical UI) which allows it to perceive external informations,
and its influence is like a textbox or colored gauge through which the component
can transmit informations (and do an influence) to the outside. The view of an
agent is then the agent’s interactors (which allow influence and perception) with
the environment in a particular realm.
3.4 An Agent with aMVC
If we used the upper definition of aMVC, our agent will be a many-layered aMVC
component, where each aMVC component is related to a realm. In the Table 2,
we can see one layer of an agent in an aMVC form. This way, if we are taking
one aMVC layer for each realm, we are then able to make a many-layered aMVC
agent such as in Figure 2a.
When we compare the Figure 1a (of a basic agent) and the Figure 2a (of an
aMVC agent by following the complement of methodology we proposed in this
238 Y. Gangat, D. Payet, and R. Courdier
(a) Layers Modelization of the complex
(b) An agent Wolf with aMVC
Fig. 3. Layers Modelization and an example
paper), we could see that we found a way to organize the agent’s components in
a way that would ease the definition, the use and reuse of it.
In our previous study case, we will have an agent Wolf modelled according to
aMVC such as in the Figure 3b.
4 Conclusion and Perspectives
This new modelization starts from the bottom (the agent) and not from the
top (the system). We sliced the agent into a ”mille-feuille”(wherealayerisa
realm) and then again according to its three components : physiognomy, behav-
iors and environment’s interaction. This leads us to split also the environment
into dynamic like we did before in DOM.
Due to the real splitting we would be able to give the layer to any experts,
and if necessary divide the work among different experts thanks to the aMVC
slicing by giving any part of the layer (M, V or C). This modelization help us
in the creation of the agent. Additionally using the (a)MVC pattern’s property,
among other advantages, we would be able to make easy the reuse as well as the
customization of any part of any layer of an agent.
This approach allows us to perceive a new field of investigation, particularly
in a global level of layers’ organization and the potential dynamic evolution of its
interconnections, but also in aMVC: How far are the similarities between MVC
and aMVC? Would we be able to apply variations of MVC to aMVC? etc. The
study of this field will be the subject of further researches.
Methodology for a New Agent Architecture Based on the MVC Pattern 239
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In this paper we propose a framework, named DAMMASS, standing for Decreasing Abstraction Methodology for Multi-Agent Social Simulation, elaborated for the design and the implementation of individual-based social models. Its main characteristic is modularity. It recovers two major features. The first feature is the modularity of the modelling process: following the decreasing abstraction methodology uses a collection of models growing from very simple and abstract models to more complex and realistic ones. The second feature deals with the modularity of a given model in the frame of social and ecological modelling, it is described in term of four kinds of sub-models: Agent, Relation, Environment and Organization. The implementation of the framework will enable the management of the modelling process as well as the management of each sub-model.
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Résumé En matière d'aménagement du territoire, l'île de La Réunion est confrontée au défi d'accueillir une population de plus en plus importante tout en valorisant son terroir agricole et en proté-geant ses paysages exceptionnels. Dans un tel contexte, faire de la prospective territoriale, et en ce sens parvenir à modéliser et simuler des comportements spatialisés, prend une impor-tance capitale. Le modèle DS que nous présentons ici est un modèle de simulation d'évolution des espaces fonciers à La Réunion. Implémenté sur la plate-forme de simulation multi-agent GEAMAS-NG, il permet de simuler sur l'ensemble de l'île les interactions entre les trois grandes classes d'uti-lisation du sol (espaces naturels, agricoles et ur-bains) et d'observer les évolutions induites. Ce modèle repose sur une spécificité que nous avons élaborée : le couplage de plusieurs dyna-miques comportementales liées, à l'échelle mi-cro, à l'évolution démographique et, à l'échelle macro, à l'évolution du mode d'occupation des sols. Mots-clés : Simulation multi-agents, aména-gement du territoire, applications, dynamiques comportementales, environnement. Abstract With regard to land development, Réunion Is-land is facing the challenge of accomodating an increasingly important population and in the same time preserving its agricultural soil and its exceptional landscapes. In this context, working on territorial futurology, and in this purpose creating models and simulation tools for spatia-lized behaviors, takes a capital importance. The DS model that we present in this paper is a model of simulation of land use evolution in Réunion Island. Implemented on the multiagent simulation platform GEAMAS-NG, it simulates the interactions between the three major classes of land use (natural, agricultural and urban spaces) and shows us the induced evolutions on the island. This model is based on a specificity : the cou-pling, via the environment, of several dynamics concerning demographic trends at the micro scale and the evolution of the ground utilization type at the macro scale.
Defense-related simulation efforts are increasingly focused on component-based simulation development. This focus is reflected in efforts to design new simulation software in terms of interacting components that can be understood and reused in isolation or collectively and to modify legacy simulators to incorporate new component-based simulation technology. A strong, new capability to modify simulation software is necessary to make component-based simulation development a reality. Such a capability can only be realized by incorporating sound modeling and simulation principles and a clear separation of concerns between models, simulators, and distributed computing. This paper presents a design pattern that supports the construction of adaptable simulation software via an extension of the Model/View/Control design pattern. The resulting Model/Simulator/View/Control pattern incorporates key concepts from the DEVS modeling and simulation methodology in order to promote a separation of modeling, simulation, and distributed computing issues. The advantage of this approach to simulation software design is considered in the context of other documented attempts to promote component based simulation development. The new design pattern is demonstrated through its application in a simulation based test signal generator used to support the Single Integrated Air Picture (SIAP) systems engineering effort.
Conference Paper
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